{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"classification_images_fashionMNIST.ipynb","provenance":[{"file_id":"1XzhorsaQwoUvMJyohuI8wZwoYLsTbSsB","timestamp":1569114417610}],"collapsed_sections":[],"machine_shape":"hm"},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"CcDFB3Bk55Ey","colab_type":"code","outputId":"2bebaa3c-0134-4806-a140-5c35aeb9b330","executionInfo":{"status":"ok","timestamp":1569170755894,"user_tz":240,"elapsed":1967,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["%pylab inline\n","import numpy as np\n","import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","import torch.utils.data.dataloader as dataloader\n","import torch.optim as optim\n","\n","from torch.utils.data import TensorDataset\n","from torch.autograd import Variable\n","from torchvision import transforms\n","from torchvision.datasets import MNIST\n","\n","import torchvision\n","SEED = 1\n","\n","# CUDA?\n","cuda = torch.cuda.is_available()\n","\n","# For reproducibility\n","torch.manual_seed(SEED)\n","\n","if cuda:\n","    torch.cuda.manual_seed(SEED)"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Populating the interactive namespace from numpy and matplotlib\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"WDmAvekAs6S-","colab_type":"code","outputId":"f9e57558-b491-4158-930c-10c63daaecf9","executionInfo":{"status":"ok","timestamp":1569170779137,"user_tz":240,"elapsed":25188,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":122}},"source":["from google.colab import drive\n","drive.mount('/content/drive/')"],"execution_count":2,"outputs":[{"output_type":"stream","text":["Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n","\n","Enter your authorization code:\n","··········\n","Mounted at /content/drive/\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"gLHCEEBp5-wH","colab_type":"code","outputId":"e23d4c6a-cd75-481b-a09e-9df8f01774b7","executionInfo":{"status":"ok","timestamp":1569170787630,"user_tz":240,"elapsed":4486,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":275}},"source":["# train = MNIST('./data', train=True, download=True, transform=transforms.Compose([\n","#     transforms.ToTensor(), # ToTensor does min-max normalization. \n","# ]), )\n","\n","# test = MNIST('./data', train=False, download=True, transform=transforms.Compose([\n","#     transforms.ToTensor(), # ToTensor does min-max normalization. \n","# ]), )\n","\n","# # Create DataLoader\n","# dataloader_args = dict(shuffle=True, batch_size=256,num_workers=4, pin_memory=True) if cuda else dict(shuffle=True, batch_size=64)\n","# train_loader = dataloader.DataLoader(train, **dataloader_args)\n","# test_loader = dataloader.DataLoader(test, **dataloader_args)\n","\n","\n","# https://www.marktechpost.com/2019/07/30/introduction-to-image-classification-using-pytorch-to-classify-fashionmnist-dataset/\n","\n","#transforming the PIL Image to tensors\n","trainset = torchvision.datasets.FashionMNIST(root = \"./data\", train = True, download = True, transform = transforms.ToTensor())\n","testset = torchvision.datasets.FashionMNIST(root = \"./data\", train = False, download = True, transform = transforms.ToTensor())\n"],"execution_count":3,"outputs":[{"output_type":"stream","text":["\r0it [00:00, ?it/s]"],"name":"stderr"},{"output_type":"stream","text":["Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to ./data/FashionMNIST/raw/train-images-idx3-ubyte.gz\n"],"name":"stdout"},{"output_type":"stream","text":["26427392it [00:01, 14296482.31it/s]                             \n"],"name":"stderr"},{"output_type":"stream","text":["Extracting ./data/FashionMNIST/raw/train-images-idx3-ubyte.gz\n"],"name":"stdout"},{"output_type":"stream","text":["\r0it [00:00, ?it/s]"],"name":"stderr"},{"output_type":"stream","text":["Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to ./data/FashionMNIST/raw/train-labels-idx1-ubyte.gz\n"],"name":"stdout"},{"output_type":"stream","text":["32768it [00:00, 100210.39it/s]                           \n","0it [00:00, ?it/s]"],"name":"stderr"},{"output_type":"stream","text":["Extracting ./data/FashionMNIST/raw/train-labels-idx1-ubyte.gz\n","Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to ./data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz\n"],"name":"stdout"},{"output_type":"stream","text":["4423680it [00:01, 4281120.05it/s]                             \n","0it [00:00, ?it/s]"],"name":"stderr"},{"output_type":"stream","text":["Extracting ./data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz\n","Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to ./data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz\n"],"name":"stdout"},{"output_type":"stream","text":["8192it [00:00, 33894.54it/s]            "],"name":"stderr"},{"output_type":"stream","text":["Extracting ./data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz\n","Processing...\n","Done!\n"],"name":"stdout"},{"output_type":"stream","text":["\n"],"name":"stderr"}]},{"cell_type":"code","metadata":{"id":"jlvCky_chpsl","colab_type":"code","colab":{}},"source":["#loading the training data from trainset\n","# trainloader = torch.utils.data.DataLoader(trainset, batch_size=4, shuffle = True)\n","# #loading the test data from testset\n","# testloader = torch.utils.data.DataLoader(testset, batch_size=4, shuffle=False)\n","\n","\n","dataloader_args = dict(shuffle=True, batch_size=256,num_workers=4, pin_memory=True) if cuda else dict(shuffle=True, batch_size=64)\n","train_loader = dataloader.DataLoader(trainset, **dataloader_args)\n","test_loader = dataloader.DataLoader(testset, **dataloader_args)\n","\n","\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"LkwNvd7KhuWr","colab_type":"code","colab":{}},"source":["classes = ('T-Shirt','Trouser','Pullover','Dress','Coat','Sandal','Shirt','Sneaker','Bag','Ankle Boot')\n","def imshow(img):\n","     npimg = img.numpy() #convert the tensor to numpy for displaying the image\n","     #for displaying the image, shape of the image should be height * width * channels \n","     plt.imshow(np.transpose(npimg, (1, 2, 0))) \n","     plt.show()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"wZMt-WPJiDGj","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":164},"outputId":"b3920159-df7d-4417-ed1b-02da1ce93afa","executionInfo":{"status":"error","timestamp":1569115688953,"user_tz":240,"elapsed":850,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}}},"source":["imshow(torchvision.utils.make_grid(images))"],"execution_count":65,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-65-c43a856eb493>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorchvision\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_grid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimages\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mNameError\u001b[0m: name 'images' is not defined"]}]},{"cell_type":"code","metadata":{"id":"gQMoALj_lUvg","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"outputId":"95f09935-4413-49c6-b66b-faf21fd3b34b","executionInfo":{"status":"ok","timestamp":1569115744258,"user_tz":240,"elapsed":824,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}}},"source":["next(iter(trainloader))[0].shape"],"execution_count":70,"outputs":[{"output_type":"execute_result","data":{"text/plain":["torch.Size([4, 1, 28, 28])"]},"metadata":{"tags":[]},"execution_count":70}]},{"cell_type":"code","metadata":{"id":"gyySx9TyRoDo","colab_type":"code","outputId":"a17b9d81-7ab3-45fd-db6b-9b512c20be22","executionInfo":{"status":"error","timestamp":1569114740929,"user_tz":240,"elapsed":796,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":164}},"source":["test.data.shape"],"execution_count":9,"outputs":[{"output_type":"error","ename":"AttributeError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)","\u001b[0;32m<ipython-input-9-94c4dbacdd88>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mAttributeError\u001b[0m: 'PytestTester' object has no attribute 'data'"]}]},{"cell_type":"code","metadata":{"id":"TImqkHmNoSgK","colab_type":"code","colab":{}},"source":["class LeNet(nn.Module):\n","     def __init__(self):\n","         super(LeNet, self).__init__()\n","         self.cnn_model = nn.Sequential(\n","             nn.Conv2d(1, 6, kernel_size = 5), #(N, 1, 28, 28) -> (N, 6, 24, 24)\n","             nn.Tanh(),\n","             nn.AvgPool2d(2, stride = 2), #(N, 6, 24, 24) -> (N, 6, 12, 12)\n","             nn.Conv2d(6, 16, kernel_size = 5), #(N, 6, 12, 12) -> (N, 6, 8, 8)\n","             nn.Tanh(),\n","             nn.AvgPool2d(2, stride = 2)) #(N, 6, 8, 8) -> (N, 16, 4, 4)\n","#          self.cnn_model = nn.Sequential(\n","         self.fc_model = nn.Sequential(\n","             nn.Linear(256, 120), # (N, 256) -> (N, 120)\n","             nn.Tanh(),\n","             nn.Linear(120, 84), # (N, 120) -> (N, 84)\n","             nn.Tanh(),\n","             nn.Linear(84, 10))  # (N, 84)  -> (N, 10)) #10 classes\n","      \n","     def forward(self, x):\n","           x = self.cnn_model(x)     \n","           x = x.view(x.size(0), -1)     \n","           x = self.fc_model(x)     \n","           return x\n","          \n","# # model = Model()\n","# # if cuda:\n","# #     model.cuda() # CUDA!\n","# # optimizer = optim.Adam(model.parameters(), lr=1e-3)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"l8P08HhX6GHT","colab_type":"code","colab":{}},"source":["class Model(nn.Module):\n","    def __init__(self):\n","        super(Model, self).__init__()\n","        self.conv1 = nn.Conv2d(1, 40, 5, 1)\n","        self.conv2 = nn.Conv2d(40, 50, 5, 1)\n","        self.fc1 = nn.Linear(4*4*50, 1000)\n","        self.fc2 = nn.Linear(1000, 10)\n","\n","    def forward(self, x):\n","        x = F.relu(self.conv1(x))\n","        x = F.max_pool2d(x, 2, 2)\n","        x = F.relu(self.conv2(x))\n","        x = F.max_pool2d(x, 2, 2)\n","        x = x.view(-1, 4*4*50)\n","        x = F.relu(self.fc1(x))\n","        x = self.fc2(x)\n","        return F.softmax(x, dim=1)\n","    \n","model = Model()\n","if cuda:\n","    model.cuda() # CUDA!\n","optimizer = optim.Adam(model.parameters(), lr=1e-3)    \n","# print(torch.cuda.device_count())\n","# device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"0uZKS_Yjig9N","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":232},"outputId":"e0e2de9b-431c-4b3c-bd31-273c49da9f74","executionInfo":{"status":"error","timestamp":1569170811842,"user_tz":240,"elapsed":383,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}}},"source":["# #create the model object and move it to GPU\n","device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n","\n","model = LeNet().to(device)\n","loss_fn = nn.CrossEntropyLoss()\n","opt = optim.Adam(net.parameters())"],"execution_count":7,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-7-3d913da49549>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mdevice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"cuda:0\"\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_available\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m\"cpu\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLeNet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mloss_fn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCrossEntropyLoss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mopt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moptim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAdam\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name 'LeNet' is not defined"]}]},{"cell_type":"code","metadata":{"id":"-8RawtKG5RhW","colab_type":"code","outputId":"cf60c55b-beae-4eeb-abf2-97aa9264272d","executionInfo":{"status":"ok","timestamp":1569170824102,"user_tz":240,"elapsed":285,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":306}},"source":["from torchsummary import summary\n","# device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n","# vgg = models.vgg16().to(device)\n","# summary(net, (1, 28, 28))\n","summary(model, (1, 28, 28))"],"execution_count":10,"outputs":[{"output_type":"stream","text":["----------------------------------------------------------------\n","        Layer (type)               Output Shape         Param #\n","================================================================\n","            Conv2d-1           [-1, 40, 24, 24]           1,040\n","            Conv2d-2             [-1, 50, 8, 8]          50,050\n","            Linear-3                 [-1, 1000]         801,000\n","            Linear-4                   [-1, 10]          10,010\n","================================================================\n","Total params: 862,100\n","Trainable params: 862,100\n","Non-trainable params: 0\n","----------------------------------------------------------------\n","Input size (MB): 0.00\n","Forward/backward pass size (MB): 0.21\n","Params size (MB): 3.29\n","Estimated Total Size (MB): 3.50\n","----------------------------------------------------------------\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"kCTxYRTR6KNS","colab_type":"code","outputId":"bc3d4324-352a-4b74-e80d-3c7f1912e090","executionInfo":{"status":"ok","timestamp":1569170894302,"user_tz":240,"elapsed":62462,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":340}},"source":["EPOCHS = 15\n","losses = []\n","\n","model.train()\n","for epoch in range(EPOCHS):\n","    for batch_idx, (data, target) in enumerate(train_loader):\n","        # Get Samples\n","        data, target = Variable(data), Variable(target)\n","        \n","        if cuda:\n","            data, target = data.cuda(), target.cuda()\n","        \n","        # Init\n","        optimizer.zero_grad()\n","\n","        # Predict\n","        y_pred = model(data) \n","\n","        # Calculate loss\n","        loss = F.cross_entropy(y_pred, target)\n","        losses.append(loss.cpu().data)\n","#         losses.append(loss.cpu().data[0])        \n","        # Backpropagation\n","        loss.backward()\n","        optimizer.step()\n","        \n","        \n","        # Display\n","        if batch_idx % 100 == 1:\n","            print('\\r Train Epoch: {}/{} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n","                epoch+1,\n","                EPOCHS,\n","                batch_idx * len(data), \n","                len(train_loader.dataset),\n","                100. * batch_idx / len(train_loader), \n","                loss.cpu().data), \n","                end='')\n","    # Eval\n","    evaluate_x = Variable(test_loader.dataset.test_data.type_as(torch.FloatTensor()))\n","    evaluate_y = Variable(test_loader.dataset.test_labels)\n","    if cuda:\n","        evaluate_x, evaluate_y = evaluate_x.cuda(), evaluate_y.cuda()\n","\n","    model.eval()\n","    output = model(evaluate_x[:,None,...])\n","    pred = output.data.max(1)[1]\n","    d = pred.eq(evaluate_y.data).cpu()\n","    accuracy = d.sum().type(dtype=torch.float64)/d.size()[0]\n","    \n","    print('\\r Train Epoch: {}/{} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}\\t Test Accuracy: {:.4f}%'.format(\n","        epoch+1,\n","        EPOCHS,\n","        len(train_loader.dataset), \n","        len(train_loader.dataset),\n","        100. * batch_idx / len(train_loader), \n","        loss.cpu().data,\n","        accuracy*100,\n","        end=''))\n","\n","\n","\n","# # function to do evaluation (calculate the accuracy) in gpu\n","# def evaluation(dataloader):\n","#      total, correct = 0, 0\n","#      #keeping the network in evaluation mode \n","#      net.eval()\n","#      for data in dataloader:\n","#          inputs, labels = data\n","#          #moving the inputs and labels to gpu\n","#          inputs, labels = inputs.to(device), labels.to(device)\n","#          outputs = net(inputs)\n","#          _, pred = torch.max(outputs.data, 1)\n","#          total += labels.size(0)\n","#          correct += (pred == labels).sum().item()\n","#      return 100 * correct / total"],"execution_count":11,"outputs":[{"output_type":"stream","text":[" Train Epoch: 1/15 [60000/60000 (100%)]\tLoss: 1.709010\t Test Accuracy: 77.2800%\n"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:58: UserWarning: test_data has been renamed data\n","  warnings.warn(\"test_data has been renamed data\")\n","/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:48: UserWarning: test_labels has been renamed targets\n","  warnings.warn(\"test_labels has been renamed targets\")\n"],"name":"stderr"},{"output_type":"stream","text":[" Train Epoch: 2/15 [60000/60000 (100%)]\tLoss: 1.627890\t Test Accuracy: 78.6100%\n"," Train Epoch: 3/15 [60000/60000 (100%)]\tLoss: 1.601566\t Test Accuracy: 84.9700%\n"," Train Epoch: 4/15 [60000/60000 (100%)]\tLoss: 1.597577\t Test Accuracy: 85.0000%\n"," Train Epoch: 5/15 [60000/60000 (100%)]\tLoss: 1.576257\t Test Accuracy: 86.8500%\n"," Train Epoch: 6/15 [60000/60000 (100%)]\tLoss: 1.581382\t Test Accuracy: 82.9400%\n"," Train Epoch: 7/15 [60000/60000 (100%)]\tLoss: 1.608374\t Test Accuracy: 86.2000%\n"," Train Epoch: 8/15 [60000/60000 (100%)]\tLoss: 1.549301\t Test Accuracy: 84.3000%\n"," Train Epoch: 9/15 [60000/60000 (100%)]\tLoss: 1.517467\t Test Accuracy: 84.5400%\n"," Train Epoch: 10/15 [60000/60000 (100%)]\tLoss: 1.575722\t Test Accuracy: 85.8100%\n"," Train Epoch: 11/15 [60000/60000 (100%)]\tLoss: 1.514408\t Test Accuracy: 87.6100%\n"," Train Epoch: 12/15 [60000/60000 (100%)]\tLoss: 1.538979\t Test Accuracy: 87.2000%\n"," Train Epoch: 13/15 [60000/60000 (100%)]\tLoss: 1.561146\t Test Accuracy: 87.0600%\n"," Train Epoch: 14/15 [60000/60000 (100%)]\tLoss: 1.551156\t Test Accuracy: 87.3000%\n"," Train Epoch: 15/15 [60000/60000 (100%)]\tLoss: 1.538429\t Test Accuracy: 86.6100%\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"35n9_KqYfGX5","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":375},"outputId":"c04f6dfa-dd8b-490c-d9eb-4503920df93e","executionInfo":{"status":"error","timestamp":1569115437743,"user_tz":240,"elapsed":813,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}}},"source":["# # torch.save(model,os.path.join(save_path, 'cnn.pth'))\n","# # torch.save(model.state_dict(), os.path.join(save_path, 'cnn_state.pth'))\n","\n","# # %%time\n","#  loss_arr = []\n","#  loss_epoch_arr = []\n","#  max_epochs = 10\n","#  for epoch in range(max_epochs):\n","#      #iterate through all the batches in each epoch\n","#      for i, data in enumerate(trainloader, 0):\n","#      #keeping the network in training mode     \n","#        net.train()     \n","#        inputs, labels = data     \n","#        #moving the input and labels to gpu     \n","#        inputs, labels = inputs.to(device), labels.to(device)     \n","#        #clear the gradients     \n","#        opt.zero_grad()     \n","#        #forward pass     \n","#        outputs = net(inputs)      \n","#        loss = loss_fn(outputs, labels)     \n","#        #backward pass     \n","#        loss.backward()     \n","#        opt.step()     \n","#        loss_arr.append(loss.item())\n","#      loss_epoch_arr.append(loss.item()) "],"execution_count":44,"outputs":[{"output_type":"error","ename":"RuntimeError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)","\u001b[0;32m<ipython-input-44-058d0bb4ad10>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     13\u001b[0m       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pass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    491\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    492\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 493\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m 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\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    491\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    492\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 493\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    494\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    495\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    336\u001b[0m                             _pair(0), self.dilation, self.groups)\n\u001b[1;32m    337\u001b[0m         return F.conv2d(input, self.weight, self.bias, self.stride,\n\u001b[0;32m--> 338\u001b[0;31m                         self.padding, self.dilation, self.groups)\n\u001b[0m\u001b[1;32m    339\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    340\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mRuntimeError\u001b[0m: Given groups=1, weight of size 6 3 5 5, expected input[4, 1, 28, 28] to have 3 channels, but got 1 channels instead"]}]},{"cell_type":"code","metadata":{"id":"wIcKVDJJt6VM","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"outputId":"d33a1ac1-6daf-4ffd-feea-9f0a30b0a22b","executionInfo":{"status":"ok","timestamp":1569116199724,"user_tz":240,"elapsed":811,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}}},"source":["inputs[2].shape"],"execution_count":86,"outputs":[{"output_type":"execute_result","data":{"text/plain":["torch.Size([1, 28, 28])"]},"metadata":{"tags":[]},"execution_count":86}]},{"cell_type":"code","metadata":{"id":"v67pk3_Ykh3o","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":286},"outputId":"4a24b22c-b4e6-4e02-e1fa-447676234b00","executionInfo":{"status":"ok","timestamp":1569116202916,"user_tz":240,"elapsed":924,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}}},"source":["plt.imshow(inputs[0,0].cpu().numpy())"],"execution_count":87,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7f36ebfffa90>"]},"metadata":{"tags":[]},"execution_count":87},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"7ind_L9ifSmr","colab_type":"code","outputId":"9cd6d84f-378d-4e7d-cdae-5116af76c22c","executionInfo":{"status":"error","timestamp":1566147934290,"user_tz":240,"elapsed":440,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":164}},"source":["save_path"],"execution_count":0,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-6-29d307d65113>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msave_path\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mNameError\u001b[0m: name 'save_path' is not defined"]}]},{"cell_type":"code","metadata":{"id":"uhz_O9SP6N2C","colab_type":"code","outputId":"69405519-782c-489e-b91c-39e4f5cb9970","executionInfo":{"status":"ok","timestamp":1569170905558,"user_tz":240,"elapsed":382,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":102}},"source":["evaluate_x = Variable(test_loader.dataset.test_data.type_as(torch.FloatTensor()))\n","evaluate_y = Variable(test_loader.dataset.test_labels)\n","if cuda:\n","    evaluate_x, evaluate_y = evaluate_x.cuda(), evaluate_y.cuda()\n","\n","model.eval()\n","output = model(evaluate_x[:,None,...])\n","pred = output.data.max(1)[1]\n","d = pred.eq(evaluate_y.data).cpu()\n","accuracy = d.sum().type(dtype=torch.float64)/d.size()[0]\n","\n","print('Accuracy:', accuracy*100)"],"execution_count":12,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:58: UserWarning: test_data has been renamed data\n","  warnings.warn(\"test_data has been renamed data\")\n","/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:48: UserWarning: test_labels has been renamed targets\n","  warnings.warn(\"test_labels has been renamed targets\")\n"],"name":"stderr"},{"output_type":"stream","text":["Accuracy: tensor(86.6100, dtype=torch.float64)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"UpG_rs_Jt8MC","colab_type":"code","outputId":"a1b3791d-bb91-4bc2-af8d-e25bbb8c024c","executionInfo":{"status":"ok","timestamp":1569116128537,"user_tz":240,"elapsed":1051,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":187}},"source":["import sklearn\n","from sklearn import metrics\n","sklearn.metrics.confusion_matrix(pred.cpu().numpy(), evaluate_y.cpu().numpy())"],"execution_count":84,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[856,   2,  20,  26,   0,  20, 138,   8,   3,   0],\n","       [  2, 980,   5,  12,   1,   1,   3,   2,   2,   0],\n","       [ 16,   0, 681,  10,  12,  78,  34,   9,   4,   1],\n","       [ 20,  13,  13, 885,  33,   4,  28,   6,   6,   3],\n","       [ 10,   2, 191,  32, 921,   0, 190,   1,   9,   0],\n","       [  0,   0,   0,   0,   0, 846,   1,  33,   0,   4],\n","       [ 77,   1,  87,  28,  30,   9, 593,  12,   7,   2],\n","       [  0,   0,   0,   0,   0,   7,   0, 867,   0,  14],\n","       [ 19,   2,   3,   6,   3,  21,  13,   8, 969,   2],\n","       [  0,   0,   0,   1,   0,  14,   0,  54,   0, 974]])"]},"metadata":{"tags":[]},"execution_count":84}]},{"cell_type":"code","metadata":{"id":"rREJUjpnTecf","colab_type":"code","colab":{}},"source":["# uploading the GAN trained model\n","\n","save_path = 'drive/My Drive/classification_images'\n","\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"90lwqE6bkAQz","colab_type":"code","outputId":"ce870cb9-f81b-4201-cee7-bc197bd1750b","executionInfo":{"status":"ok","timestamp":1566069728100,"user_tz":240,"elapsed":1184,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":286}},"source":[" z = torch.rand(10, 1, 28, 28) #.cuda()\n"," z[z<.5] = 0\n"," z[z>=.5] = 1\n","  \n"," plt.imshow(z[1].reshape(28,28), cmap = 'gray')"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7f66508938d0>"]},"metadata":{"tags":[]},"execution_count":13},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAADYxJREFUeJzt3V+oJGedxvHnMepN9CJZj8MQJ0Yl\nCCHg6GmGBYMoqMQgTLwJ5mIZQTxeGFDwwhAvNpdB/IMXIhzN4CgaXdCQuQir2UHILiySPiHm766J\n4YgzTGbOMILxyk3y24tTIyfJ6a5OV731Vs/v+4HmdFd3Vf26up9T3f1Wva8jQgDyeVPtAgDUQfiB\npAg/kBThB5Ii/EBShB9IivADSRF+ICnCDyT15iFXZrvY4YTr6+tz79/a2iq16s661j5v/i7z9mHM\n272kku/Hecve3t7WhQsXvMhy3OXwXts3S/qupCsk/TAi7ml5fLHwtz0Pe6HtUUXX2ufN32XePox5\nu5dU8v04b9mTyUTT6XShhS/9sd/2FZK+J+lTkm6QdLvtG5ZdHoBhdfnOf0TScxHxfET8XdLPJR3t\npywApXUJ/zWS/rzn9ulm2qvY3rA9tT3tsC4APSv+g19EbEralMp+5wfwxnTZ85+RdGjP7Xc10wCs\ngC7hf0TS9bbfY/utkj4r6WQ/ZQEobemP/RHxku07JP1au019xyPiqXnzrK+vazqd/dV/zE1apZpm\n+ph/Xm01n3fb+ru+ZiVf867Pu3YT6yI6feePiAclPdhTLQAGxOG9QFKEH0iK8ANJEX4gKcIPJEX4\ngaQGPZ+/Tc327C4u57b0ks9tDG3ds4y5tr6w5weSIvxAUoQfSIrwA0kRfiApwg8kNaqmvjH39Fry\n9NCSzUpj7p13FU57naVk82zpdV/Cnh9IivADSRF+ICnCDyRF+IGkCD+QFOEHkhq0nX9ra6taF9g1\n22VLW9VuxduU7j67i7F2BT+ZTBZeDnt+ICnCDyRF+IGkCD+QFOEHkiL8QFKEH0iqUzu/7W1JL0p6\nWdJLETG3kbHrEN0ttSw9r1S22/CSbeFdjXmY7DY1h+gec98Ti+rjIJ+PRcSFHpYDYEB87AeS6hr+\nkPQb21u2N/ooCMAwun7svykizth+p6SHbP9PRDy89wHNP4UNSbr22ms7rg5AXzrt+SPiTPP3vKT7\nJR3Z5zGbETGJiMna2lqX1QHo0dLht32l7bdfui7pk5Ke7KswAGV1+dh/QNL9TZPHmyX9LCL+vZeq\nABS3dPgj4nlJH+ixlk66tkePud225nECJc+pH/PxD7WHXR8CTX1AUoQfSIrwA0kRfiApwg8kRfiB\npEY1RHcXY+6au+Zps6s8DHaby7mpcB6G6AbQCeEHkiL8QFKEH0iK8ANJEX4gKcIPJOUh23ltV2tU\npk14uXXXPH6iZO21n3fh4cMXWjh7fiApwg8kRfiBpAg/kBThB5Ii/EBShB9IaqXO5+9y3vrlfF57\nF1mHBy/9fijcjj/zvslksvBy2PMDSRF+ICnCDyRF+IGkCD+QFOEHkiL8QFKt7fy2j0v6tKTzEXFj\nM+1qSb+QdJ2kbUm3RcRfuhbTpW21ZrvsmI8RWOV+DEoOD9513pKv+VDvp0X2/D+SdPNrpt0p6VRE\nXC/pVHMbwAppDX9EPCzp4msmH5V0orl+QtKtPdcFoLBlv/MfiIizzfUXJB3oqR4AA+l8bH9ExLy+\n+WxvSNrouh4A/Vp2z3/O9kFJav6en/XAiNiMiElELH7GAYDilg3/SUnHmuvHJD3QTzkAhtIaftv3\nSfpvSe+3fdr25yXdI+kTtp+V9PHmNoAVQr/9PRjzueFjPgahtJr99rfp0tfAAsum334AsxF+ICnC\nDyRF+IGkCD+QFOEHkhpV190lh1zuasynvs5Ts26pbvfZ89TsNrxt/qGaIdnzA0kRfiApwg8kRfiB\npAg/kBThB5Ii/EBSo2rnr2nMQ1V3Ubq9uk3JYzNWedj1kqf0Loo9P5AU4QeSIvxAUoQfSIrwA0kR\nfiApwg8kNWg7//r6uqbT6dLzj/W89tpt6V3WXXr+Lsse8zEGXdbddf3z5p1MFh8Yiz0/kBThB5Ii\n/EBShB9IivADSRF+ICnCDyTV2s5v+7ikT0s6HxE3NtPulvQFSTvNw+6KiAdLFbmnlqXnLdkXeunz\nr2seo9BVzbb2kseFdH0/jeH9tsie/0eSbt5n+nci4nBzKR58AP1qDX9EPCzp4gC1ABhQl+/8d9h+\n3PZx21f1VhGAQSwb/u9Lep+kw5LOSvrWrAfa3rA9tT3d2dmZ9TAAA1sq/BFxLiJejohXJP1A0pE5\nj92MiElETNbW1patE0DPlgq/7YN7bn5G0pP9lANgKIs09d0n6aOS3mH7tKR/lfRR24clhaRtSV8s\nWCOAAlrDHxG37zP53gK1FD0HuuZ46m0u59q69E8/5n4OauJ8fgCdEH4gKcIPJEX4gaQIP5AU4QeS\nGtUQ3SVPu61plbvP7jp/zebZLvN2PSW35PMe8pReAJchwg8kRfiBpAg/kBThB5Ii/EBShB9IyqW7\nT37VyuxOK6t5emjNbsPHfEpvmy6vWZdlr7IetstCC2DPDyRF+IGkCD+QFOEHkiL8QFKEH0iK8ANJ\npTmff8zdRJc8DqDm825T85z6mq9Jm6H6tWDPDyRF+IGkCD+QFOEHkiL8QFKEH0iK8ANJtYbf9iHb\nv7X9tO2nbH+5mX617YdsP9v8vaptWevr64qImZc2XeZd4HnOvcxbd9ulpq61dXneXZ97yXW3zdv2\nfmjTdf4hLLLnf0nSVyPiBkn/LOlLtm+QdKekUxFxvaRTzW0AK6I1/BFxNiIeba6/KOkZSddIOirp\nRPOwE5JuLVUkgP69oe/8tq+T9EFJv5N0ICLONne9IOlAr5UBKGrh8Nt+m6RfSvpKRPx1732x+wVr\n3y9ZtjdsT21Pd3Z2OhULoD8Lhd/2W7Qb/J9GxK+ayedsH2zuPyjp/H7zRsRmREwiYrK2ttZHzQB6\nsMiv/ZZ0r6RnIuLbe+46KelYc/2YpAf6Lw9AKa1dd9u+SdJ/SnpC0ivN5Lu0+73/3yRdK+lPkm6L\niIstyyrW7rXKw3uPuVvxmkqfdltr2W3LH6rr7pXqt38ewl9n3SUR/qWXTb/9AGYj/EBShB9IivAD\nSRF+ICnCDyS1Ul1311x2yabCmt1Ad3W5NqHWHPKdrrsBFEX4gaQIP5AU4QeSIvxAUoQfSIrwA0kN\n2s6/vr6u6XQ68/6SbcY1h1wu3WZc+PTQYvOP+RiB0koduzGZTBZeDnt+ICnCDyRF+IGkCD+QFOEH\nkiL8QFKEH0gqzfn8Xdfd5fzrmmp3aX659oMw5m7FF8WeH0iK8ANJEX4gKcIPJEX4gaQIP5AU4QeS\nam3nt31I0o8lHZAUkjYj4ru275b0BUk7zUPviogH5y1ra2urWLtvzfbs0m2+qzwUdc111+wvYBX6\nKljkIJ+XJH01Ih61/XZJW7Yfau77TkR8s1x5AEppDX9EnJV0trn+ou1nJF1TujAAZb2h7/y2r5P0\nQUm/aybdYftx28dtXzVjng3bU9uz++8CMLiFw2/7bZJ+KekrEfFXSd+X9D5Jh7X7yeBb+80XEZsR\nMYmIxTsXA1DcQuG3/RbtBv+nEfErSYqIcxHxckS8IukHko6UKxNA31rD792fLe+V9ExEfHvP9IN7\nHvYZSU/2Xx6AUhb5tf/Dkv5F0hO2H2um3SXpdtuHtdv8ty3pi20Lauu6u6SSzW2X82mxbWp2aV66\nS/QuxnDKbptFfu3/L0n7PZO5bfoAxo0j/ICkCD+QFOEHkiL8QFKEH0iK8ANJjarr7jY12067dN1d\ne5jsLtpqX4X27P2Ufk3GPKT7Jez5gaQIP5AU4QeSIvxAUoQfSIrwA0kRfiApD9nWantH0p/2THqH\npAuDFfDGjLW2sdYlUduy+qzt3RGxtsgDBw3/61ZuT8fat99YaxtrXRK1LatWbXzsB5Ii/EBStcO/\nWXn984y1trHWJVHbsqrUVvU7P4B6au/5AVRSJfy2b7b9v7afs31njRpmsb1t+wnbj9UeYqwZBu28\n7Sf3TLva9kO2n23+7jtMWqXa7rZ9ptl2j9m+pVJth2z/1vbTtp+y/eVmetVtN6euKttt8I/9tq+Q\n9AdJn5B0WtIjkm6PiKcHLWQG29uSJhFRvU3Y9kck/U3SjyPixmbaNyRdjIh7mn+cV0XE10ZS292S\n/lZ75OZmQJmDe0eWlnSrpM+p4rabU9dtqrDdauz5j0h6LiKej4i/S/q5pKMV6hi9iHhY0sXXTD4q\n6URz/YR23zyDm1HbKETE2Yh4tLn+oqRLI0tX3XZz6qqiRvivkfTnPbdPa1xDfoek39jesr1Ru5h9\nHGiGTZekFyQdqFnMPlpHbh7Sa0aWHs22W2bE677xg9/r3RQRH5L0KUlfaj7ejlLsfmcbU3PNQiM3\nD2WfkaX/oea2W3bE677VCP8ZSYf23H5XM20UIuJM8/e8pPs1vtGHz10aJLX5e75yPf8wppGb9xtZ\nWiPYdmMa8bpG+B+RdL3t99h+q6TPSjpZoY7XsX1l80OMbF8p6ZMa3+jDJyUda64fk/RAxVpeZSwj\nN88aWVqVt93oRryOiMEvkm7R7i/+f5T09Ro1zKjrvZJ+31yeql2bpPu0+zHw/7T728jnJf2TpFOS\nnpX0H5KuHlFtP5H0hKTHtRu0g5Vqu0m7H+kfl/RYc7ml9rabU1eV7cYRfkBS/OAHJEX4gaQIP5AU\n4QeSIvxAUoQfSIrwA0kRfiCp/wdujbFGCPttrQAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"NthHSdBaB54v","colab_type":"code","outputId":"e311b43e-0aa9-45e1-9e1a-2708dcab5b07","executionInfo":{"status":"ok","timestamp":1566070804440,"user_tz":240,"elapsed":895,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":333}},"source":["z = torch.rand(10, 1, 28) #.cuda()\n","# z[z<.5] = 0\n","# z[z>=.5] = 1\n","  \n","plt.imshow(z[1])\n","\n","zz = torch.stack(28*[z], dim=2)\n","zz.shape\n","\n","plt.figure()\n","plt.imshow(zz[4,0], cmap = 'gray')\n","\n","\n","\n"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7f66500d1d30>"]},"metadata":{"tags":[]},"execution_count":34},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAX8AAAAvCAYAAAALvcokAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAABzRJREFUeJzt3W+MXFUdxvHvw7alsq3tFmTBdilU\nDSDGiNSKCTGNglRNWohYaIzWFwqJNsgbgxWDtQnYEBFemJigVpCIaPBfNUTEoNEoErZNbWmhpZY2\nsm53QxutrWxr258v7l2crjO7czp3OzN3nk+ymdk7z8w9p2fnzO2Zc89VRGBmZp3ljGYXwMzMTj93\n/mZmHcidv5lZB3Lnb2bWgdz5m5l1IHf+ZmYdqKHOX9IcSU9KejG/7amROy5pc/6zoZF9mplZ49TI\nPH9J9wAHImKdpC8APRFxe5XcoYiY0UA5zcysQI12/juAxRExKOl84HcRcXGVnDt/M7MW0uiYf29E\nDOb39wG9NXLTJfVL+rOk6xrcp5mZNWjKRAFJvwHOq/LQHZW/RERIqvXfiPkRMSBpAfCUpK0R8dcq\n+7oZuBmgiylXdJ8xa8IKjDp60bS6swBzX/ePpPzfh85Oyp+Y8F/2ZGcOvZqUH5k3PSl/6ezhpPzu\nkapf39R09FhahSOUlD/3rINJ+d6uo0n57YNvqDub2rbTBg8n5U/MPisp33U4ra5HLkirwFu79yfl\nd23pTspr2tSk/Mh5afkLX/9KUv5MHU/K7zxQrXusrWfWoaT8v3envdcPjux7JSIm/IM+LcM+Y57z\nIPDLiHhsvNysrnPiyhlL6y7LwMPz6s4CrL3sF0n5r9z3iaT8kTlJcebf95ek/Av3XpaU3/Th+5Py\nN+1cnpTfuz/xw2Ik7Q186xVPJeVv69mTlL/8rs/UnT0yO+ml6bv76aT8q0vflZSf+czepPxL36j/\ngw6g/8r1Sfnr5y1Kyk/pS3vvbr/jjUn5717z7aT8gqlpBxpXP/L5pPwN1/4xKb/5Y5ck5Z/YdvfG\niFg4Ua7RYZ8NwEpJS4AtwLn5F7+vkdQjaaakH0raDdwEpB12m5lZoRrt/NcB15B9COwELgFWSPqo\npNGP20uBF4D3A4eBB4FPN7hfMzNrQOLo5ckiYr+kLwFrIuJaAEmPAm+OiE/lmT9Jei7PPC1pCrBP\nksLrSZuZNUURZ/jOBf5W8fvL+baqmYg4BvwTSPsG1czMCtPQkX/RKmf7TFfajAEzM6tfEUf+A0Bf\nxe/z8m1VM/mwzyzg/+aPRcQDEbEwIhZOU9r0JjMzq18RR/7PAm/PZ/KcALqBq8dkhsjm9+8AZgMv\nebzfzKx5ijjyH+3Elf8AhKS1kkYn6v+B7Oh/BjAM3FjAfs3M7BQVceS/CNhSMdtnNbAsIu6syPwH\n+FVErCpgf2Zm1qDTNdsH4COStkh6TFJflcfNzOw0aWh5BwBJNwBLRuf1S/o48O7Ko3xJZwOHIuKI\npFuAGyPifVVe67XZPsDFwI4quzwHSFuso725vuXm+pZXs+o6f9LX9gGQ9B5OPslrNUBEfLVGvovs\nGgD1r9p28vP761m3oixc33Jzfcur1etaxLDPs8BbJF0kaRrZ2j0nXa0rX/Rt1FLg+QL2a2Zmp6jh\nL3wj4pikVcATQBewPiK2SVoL9EfEBuDWfObPMeAA8MlG92tmZqeukDN8I+Jx4PEx2+6suL8aWF3E\nvoAHCnqdduH6lpvrW14tXdeGx/zNzKz9FDHmb2ZmbaatOn9JSyTtkLRr7EVjykjSHklbJW2W1N/s\n8hRN0npJw/mS36Pb5kh6UtKL+W3aJcJaWI36rpE0kLfxZkkfamYZiyKpT9JvJW2XtE3S5/LtpWzf\ncerbsu3bNsM++RTRnWQXj3mZbJbRiojY3tSCTSJJe4CFEVHKedGS3gscAr4XEW/Lt91DNhV4Xf4B\n3xMRtzeznEWpUd81ZOfAfK2ZZStaPsPv/IjYJGkmsBG4jmyyR+nad5z6LqdF27edjvwXAbsiYndE\nHAUeBZY1uUzWgIj4Pdnsr0rLgIfy+w+RvYFKoUZ9SykiBiNiU37/X2TTu+dS0vYdp74tq506/3qX\nkSiTAH4taWN+9nMn6I2Iwfz+PqC3mYU5TVblS5+sL8swSCVJFwKXA8/QAe07pr7Qou3bTp1/J7oq\nIt4JfBD4bD5s0DHyZb/bY1zy1H0TeBPwDmAQuLe5xSmWpBnAj4HbIuJg5WNlbN8q9W3Z9m2nzr+e\ni8aUSkQM5LfDwE/Jhr7Kbmj0jPD8drjJ5ZlUETEUEccj4gTwLUrUxpKmknWE34+In+SbS9u+1erb\nyu3bTp3/hMtIlImk7vyLIyR1Ax8Anhv/WaWwAViZ318J/LyJZZl0Y5Y+uZ6StLEkAd8Bno+Ir1c8\nVMr2rVXfVm7ftpntA5BPk7qf/y0jcVeTizRpJC0gO9qH7EzsR8pWX0k/ABaTrX44BHwZ+BnwI+AC\nYC+wPCJK8SVpjfouJhsSCGAPcEvFmHjbknQV2UWctpJd4Q/gi2Tj4KVr33Hqu4IWbd+26vzNzKwY\n7TTsY2ZmBXHnb2bWgdz5m5l1IHf+ZmYdyJ2/mVkHcudvZtaB3PmbmXUgd/5mZh3ov452sqKCOChq\nAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"PAKtrQcbF0G0","colab_type":"code","outputId":"cb769a9b-646b-4c8f-b59b-0e0df8a62ea0","executionInfo":{"status":"ok","timestamp":1566073606738,"user_tz":240,"elapsed":1423,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":538}},"source":["# z = torch.rand(10, 1, 28, 28) #.cuda()\n","z = np.zeros((10, 1, 7, 7)) #.cuda()\n","# z[z<.5] = 0\n","# z[z>=.5] = 1\n","\n","idx_x = np.random.randint(0,6,(10,))\n","idx_y = np.random.randint(0,6,(10,))\n","idx_r = np.random.randint(0,9,(10,))\n","\n","# idxs = np.radnom.rand()\n","# idx1 = torch.randint(0,9,(10,2))\n","\n","# plt.imshow(z[1])\n","\n","# zz = torch.stack(28*[z], dim=2)\n","# zz.shape\n","# z[0,0,idxs[0,0], idxs[0,1]] = 1\n","# z[:,0,idxs[:0],idxs[:1]] = 1\n","z[range(10),0,idx_x,idx_y] = 1\n","\n","z = torch.from_numpy(z)\n","upsample = nn.Upsample(scale_factor=4, mode='nearest')\n","z = upsample(z)\n","\n","\n","plt.figure()\n","plt.imshow(z[4,0])\n","# idxs[:,1]\n","plt.figure()\n","plt.imshow(z[2,0])\n"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7f663d9c7c18>"]},"metadata":{"tags":[]},"execution_count":105},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nWgCl0EcPAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nN7SWAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"Eq97UPy0CXsk","colab_type":"code","outputId":"f6691295-e4ef-4c05-bb83-3f3ea0da7ec2","executionInfo":{"status":"ok","timestamp":1566072730104,"user_tz":240,"elapsed":1227,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# z.shape\n","np.random.rand(5,3)\n","\n","# ar = np.random.rand(2, 3, 5)\n","# xs = np.array([0, 1, 0, 0, 1])\n","# ys = np.array([0, 1, 2, 3, 4])\n","# res = np.random.rand(5,3)\n","ar[xs, :, ys].shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(5, 3)"]},"metadata":{"tags":[]},"execution_count":79}]},{"cell_type":"code","metadata":{"id":"PSGl_gUQNWzC","colab_type":"code","outputId":"9881b6f8-789b-404d-dfac-a5a1c2ed386c","executionInfo":{"status":"ok","timestamp":1566072767983,"user_tz":240,"elapsed":458,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["ar[0,...].shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(3, 5)"]},"metadata":{"tags":[]},"execution_count":83}]},{"cell_type":"code","metadata":{"id":"OTcUTGTVjW2X","colab_type":"code","colab":{}},"source":["# # black and white patterns\n","\n","# batch_size = 10000\n","# all_size = 100000\n","\n","\n","# iters = 10\n","\n","# stats = dict()\n","# for i in range(10):\n","#     stats[i] = 0\n","    \n","    \n","# avgs = torch.zeros(iters, 10, 28*28)\n","\n","# for kk in range(iters):\n","#   print(kk)\n","  \n","# #   z = torch.rand(all_size, 1, 28, 28)*2 -1 #.cuda()\n","#   z = torch.rand(all_size, 1, 28, 28) #.cuda()\n","# #   z[z<.5] = 0\n","# #   z[z<=.5] = 1\n","  \n","#   z.cuda()\n","#   #plt.imshow(z[1].reshape(28,28))\n","\n","\n","#   all_preds = []\n","#   all_confs = []\n","#   all_idx = torch.ones(all_size, dtype = torch.uint8)\n","#   for k in range(0,all_size, batch_size):\n","#       y_pred = model(z[k:k+batch_size].cuda())\n","#       #y_pred[y_pred < 0]= 0\n","\n","#       indices = torch.ones(y_pred.size(0), dtype = torch.uint8)\n","#       indices[torch.mean(y_pred, dim =1)==0] = 0 \n","\n","#       all_idx[k:k+batch_size] = indices \n","#       confs, pred = y_pred[indices==1].data.max(1)\n","      \n","#       all_preds.append(pred)\n","#       all_confs.append(confs)\n","\n","#   pred = torch.cat(all_preds)\n","#   confs = torch.cat(all_confs)\n","  \n","#   for i in range(10):\n","#     stats[i] += torch.sum(pred==i)\n","  \n","\n","#   tt = confs[:,None].repeat(1,784)\n","#   uu = z[all_idx].view(-1,28*28)\n","# #   z = z[all_idx]*confs\n","#   z = uu* tt.type(torch.FloatTensor)\n","#   z = z.view(-1,1,28,28)\n","  \n","#   for i in range(10):\n","#       a = torch.mean(z[pred==i] , dim=0) \n","#       avgs[kk, i] = a.reshape(28*28)\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"DtpO9yHdEGyQ","colab_type":"code","outputId":"2de9c789-7eea-4d2a-8bf6-4143dd14c236","executionInfo":{"status":"ok","timestamp":1566074015990,"user_tz":240,"elapsed":27716,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":187}},"source":["# with random stripes  --> did not work\n","# zz = torch.stack(28*[z], dim=2)\n","# clean one\n","\n","upsample = nn.Upsample(scale_factor=4, mode='nearest')\n","\n","\n","batch_size = 10000\n","all_size = 100000\n","iters = 10\n","\n","stats = dict()\n","for i in range(10):\n","    stats[i] = 0    \n","    \n","avgs = torch.zeros(iters, 10, 28*28)\n","\n","for kk in range(iters):\n","  print(kk)\n","  \n","#   z = torch.rand(all_size, 1, 28) #.cuda() \n","#   z = torch.stack(28*[z], dim=2)   stripes\n","  z = np.zeros((all_size, 1, 7, 7)) #.cuda()\n","\n","  idx_x = np.random.randint(0,6,(all_size,))\n","  idx_y = np.random.randint(0,6,(all_size,))\n","#   idx_r = np.random.randint(0,all_size-1,(all_size,))\n","\n","  z[range(all_size),0,idx_x,idx_y] = 1\n","\n","  z = torch.from_numpy(z)\n","  z = upsample(z)\n","  z = z.type(torch.FloatTensor)\n","  z.cuda()\n","\n","  all_preds = []\n","  all_confs = []\n","\n","  for k in range(0,all_size, batch_size):\n","      y_pred = model(z[k:k+batch_size].cuda())\n","      conf, pred = y_pred.data.max(1)\n","      \n","      \n","      all_preds.append(pred)\n","      all_confs.append(conf)\n","  \n","  preds = torch.cat(all_preds)\n","  confs = torch.cat(all_confs)    \n","  \n","#   weighting\n","  tt = confs[:,None].repeat(1,784)\n","  uu = z.view(-1,28*28)\n","  z = uu* tt.type(torch.FloatTensor)\n","  z = z.view(-1,1,28,28)\n","\n","  for i in range(10):\n","    stats[i] += torch.sum(preds==i)\n","    a = torch.mean(z[preds==i] , dim=0) \n","    avgs[kk, i] = a.reshape(28*28)\n"],"execution_count":0,"outputs":[{"output_type":"stream","text":["0\n","1\n","2\n","3\n","4\n","5\n","6\n","7\n","8\n","9\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"5umQGFJLRsaB","colab_type":"code","outputId":"84c6bef8-51ab-47fa-eb1b-76d8ca2ddfb0","executionInfo":{"status":"ok","timestamp":1566073869574,"user_tz":240,"elapsed":1172,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["z.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["torch.Size([100000, 1, 28, 28])"]},"metadata":{"tags":[]},"execution_count":107}]},{"cell_type":"code","metadata":{"id":"IXD35aAjxMal","colab_type":"code","outputId":"de700699-425b-44f2-a8bf-803265540a9f","executionInfo":{"status":"ok","timestamp":1569118096125,"user_tz":240,"elapsed":132951,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":187}},"source":["# clean one\n","\n","batch_size = 10000\n","all_size = 1000000\n","iters = 10  #(total is iters * all_size)\n","\n","stats = dict()\n","for i in range(10):\n","    stats[i] = 0    \n","    \n","avgs = torch.zeros(iters, 10, 28*28)\n","\n","for kk in range(iters):\n","  print(kk)\n","  \n","  z = torch.rand(all_size, 1, 28, 28) #.cuda()  \n","  z.cuda()\n","\n","  all_preds = []\n","  all_confs = []\n","\n","  for k in range(0,all_size, batch_size):\n","      y_pred = model(z[k:k+batch_size].cuda())\n","      conf, pred = y_pred.data.max(1)\n","      \n","      \n","      all_preds.append(pred)\n","      all_confs.append(conf)\n","  \n","  preds = torch.cat(all_preds)\n","  confs = torch.cat(all_confs)    \n","  \n","#   weighting\n","#   tt = confs[:,None].repeat(1,784)\n","#   uu = z.view(-1,28*28)\n","#   z = uu* tt.type(torch.FloatTensor)\n","  z = z.view(-1,1,28,28)\n","\n","  for i in range(10):\n","    stats[i] += torch.sum(preds==i)\n","    a = torch.mean(z[preds==i] , dim=0) \n","    avgs[kk, i] = a.reshape(28*28)\n"],"execution_count":44,"outputs":[{"output_type":"stream","text":["0\n","1\n","2\n","3\n","4\n","5\n","6\n","7\n","8\n","9\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"6goqWHuu617E","colab_type":"code","outputId":"44a3167a-d5e4-40dd-c5ca-07e510732a9e","executionInfo":{"status":"ok","timestamp":1566006188492,"user_tz":240,"elapsed":187378,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["# based on confidence\n","\n","batch_size = 10000\n","all_size = 100000\n","iters = 100\n","\n","stats = dict()\n","for i in range(10):\n","    stats[i] = 0    \n","    \n","avgs = torch.zeros(iters, 10, 28*28)\n","\n","for kk in range(iters):\n","  print(kk)\n","  \n","  z = torch.rand(all_size, 1, 28, 28) #.cuda()  \n","  z.cuda()\n","\n","  all_preds = []\n","  all_confs = []\n","  all_idx = torch.ones(all_size, dtype = torch.uint8)\n","\n","  for k in range(0,all_size, batch_size):\n","      y_pred = model(z[k:k+batch_size].cuda())\n","      \n","      # erasing the low confident ones!\n","      y_pred[y_pred <= 0.3]= 0\n","      indices = torch.ones(y_pred.size(0), dtype = torch.uint8)\n","      indices[torch.mean(y_pred, dim =1)==0] = 0 \n","      all_idx[k:k+batch_size] = indices \n","      conf, pred = y_pred[indices==1].data.max(1)#[1]\n","\n","      #if (not torch.isnan(conf).sum()) & (not torch.isnan(pred).sum())  :\n","      all_preds.append(pred)\n","      all_confs.append(conf)\n","  \n","  all_preds = torch.cat(all_preds)\n","  all_confs = torch.cat(all_confs)    \n","  \n","  # weighting\n","#   tt = confs[:,None].repeat(1,784)\n","#   uu = z.view(-1,28*28)\n","#   z = uu* tt.type(torch.FloatTensor)\n","#   z = z.view(-1,1,28,28)\n","\n","# erasing the low confident ones!\n","  z = z[all_idx]\n","    \n","  for i in range(10):\n","    stats[i] += torch.sum(all_preds==i)\n","    a = torch.mean(z[all_preds==i] , dim=0) \n","#     print(z[preds==i].shape)\n","    avgs[kk, i] = a.reshape(28*28)\n","\n","  \n","  \n","  \n","  \n","  \n","print(stats)  \n","  \n","  \n","  \n","# plotting  \n","save_path = 'drive/My Drive/classification_images/10way-unweighted'\n","import os\n","\n","# setting NANs to 0\n","avgs[avgs != avgs] = 0 \n","\n","dd = torch.mean(avgs, dim=0)\n","\n","#dd = dd - grand_mean\n","for kk in range(10):\n","  \n","  fig = plt.figure()\n","  a = dd[kk]\n","  a = a.view(-1,28)\n","  b = model(a[None,None,...].cuda())\n","\n","  #a = torch.nn.functional.log_softmax(a)# torch.nn.functional.softmax(a)\n","  conf, c = b.data.max(1) #[1]\n","  plt.title(f'gt: {str(kk)}  -  pred: {str(c.cpu().data[0].numpy())} - conf: {str(conf.cpu().data[0].numpy())} -  size-gt: {stats[kk]}')  \n","  plt.imshow(a) #, cmap = 'gray')\n","  #fig.savefig(os.path.join(save_path, str(kk)+'-.png'))\n","    \n","  "],"execution_count":0,"outputs":[{"output_type":"stream","text":["0\n","1\n","2\n","3\n","4\n","5\n","6\n","7\n","8\n","9\n","10\n","11\n","12\n","13\n","14\n","15\n","16\n","17\n","18\n","19\n","20\n","21\n","22\n","23\n","24\n","25\n","26\n","27\n","28\n","29\n","30\n","31\n","32\n","33\n","34\n","35\n","36\n","37\n","38\n","39\n","40\n","41\n","42\n","43\n","44\n","45\n","46\n","47\n","48\n","49\n","50\n","51\n","52\n","53\n","54\n","55\n","56\n","57\n","58\n","59\n","60\n","61\n","62\n","63\n","64\n","65\n","66\n","67\n","68\n","69\n","70\n","71\n","72\n","73\n","74\n","75\n","76\n","77\n","78\n","79\n","80\n","81\n","82\n","83\n","84\n","85\n","86\n","87\n","88\n","89\n","90\n","91\n","92\n","93\n","94\n","95\n","96\n","97\n","98\n","99\n","{0: tensor(52900, device='cuda:0'), 1: tensor(289, device='cuda:0'), 2: tensor(234775, device='cuda:0'), 3: tensor(36193, device='cuda:0'), 4: tensor(53767, device='cuda:0'), 5: tensor(2846, device='cuda:0'), 6: tensor(24, device='cuda:0'), 7: tensor(1435, device='cuda:0'), 8: tensor(9601045, device='cuda:0'), 9: tensor(1525, device='cuda:0')}\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAATwAAAEICAYAAADC7ki9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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K4d/eW0D7YH6dYWhmklpqei4fPcF+87+5KsbQC0rKSvmpx3X9wOJacTD46HO/7QDwf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\nCSFKhYKeEKJUKOgJIUqFgp4QolT8f4h9KVyxm6/jAAAAAElFTkSuQmCC\n","text/plain":["<Figure 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mk5GpPSFKiJT0BunYH6cASh3v+ffHQHzwtceXQVyz1qQTSKYDhhbE9KUkpzmqV1R+o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\nRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"jzznUcgVHPj9","colab_type":"code","outputId":"4eb58e2e-386c-4376-9e87-f264fe3b5240","executionInfo":{"status":"ok","timestamp":1569119517346,"user_tz":240,"elapsed":3800,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["avgs[:,6]\n","# not torch.isnan(pred).sum()\n","# stats\n","# z[preds==1]\n","(preds==6)\n","# stats\n","\n","\n","avgs[:,6].shape\n","\n","\n","f, axarr = plt.subplots(2, 5)\n","# f.set_figheight(1.4)\n","# f.set_figwidth(15)\n","f.set_figheight(7)\n","f.set_figwidth(18)\n","\n","\n","\n","\n","# plotting  \n","save_path = 'drive/My Drive/classification_images/fashionMNIST/'\n","import os\n","\n","# setting NANs to 0\n","avgs[avgs != avgs] = 0 \n","\n","dd = torch.mean(avgs, dim=0)\n","\n","#dd = dd - grand_mean\n","for kk in range(10):\n","  \n","  fig = plt.figure()\n","  a = dd[kk]\n","  \n","  a = (a -a.min()) / (a.max() -a.min())\n","  a = a.view(-1,28)\n","  \n","#   a[a<.4] = 0\n","#   a[a>=.8] = 1\n","  \n","  print(a.min(), a.max())\n","  b = model(a[None,None,...].cuda())\n","\n","  #a = torch.nn.functional.log_softmax(a)# torch.nn.functional.softmax(a)\n","  conf, c = b.data.max(1) #[1]\n","  plt.imshow(a)\n","  plt.title(f'gt: {str(kk)}  -  pred: {str(c.cpu().data[0].numpy())} - conf: {str(conf.cpu().data[0].numpy())} -  |pred|: {stats[kk]}')  \n","  fig.savefig(os.path.join(save_path, str(kk)+'-.png'))  \n","#   axarr[kk//5, kk%5].set_title(f'gt: {str(kk)}  -  pred: {str(c.cpu().data[0].numpy())} - |pred|: {stats[kk]}')  \n","#   axarr[kk//5, kk%5].imshow(a) #, cmap = 'gray')\n","#   fig.savefig(os.path.join(save_path, str(kk)+'-.png'))\n","\n","# f.savefig(os.path.join(save_path, '10M.png'))  "],"execution_count":53,"outputs":[{"output_type":"stream","text":["tensor(0.) tensor(1.)\n","tensor(0.) tensor(1.)\n","tensor(0.) tensor(1.)\n","tensor(0.) tensor(1.)\n","tensor(0.) tensor(1.)\n","tensor(0.) tensor(1.)\n","tensor(0.) tensor(1.)\n","tensor(0.) tensor(1.)\n","tensor(0.) tensor(1.)\n","tensor(0.) tensor(1.)\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABBkAAAGfCAYAAAAargqQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/tV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JPzTCxKr7fzsSbZ2pFtu/V3pxfQjTdRhPG2lBLQwhRCImGEKIQEg0hRCEkGkKIQkg0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\nhZBoCCEK8f8BzWgnNlCrEmwAAAAASUVORK5CYII=\n","text/plain":["<Figure 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Pjm5r8vgT1PdOCscXlo3aC/YNZReF+KQAwqewkN/7mxvC0/jMr/CHGzDB9/GD4rRiV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\nJEFJQUQSlBREJOH/AfabYNMK0CsuAAAAAElFTkSuQmCC\n","text/plain":["<Figure 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sCfBbYRDZzyVXu/nEz2w78AbA/X/Vyd/9GvDXHo7EnuuNxKWw6mkMjLtsYiGPf61bF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\nYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"0DFKdI6sIhWt","colab_type":"code","outputId":"2b5be420-6900-4aa0-884e-f37f1958cb7a","executionInfo":{"status":"ok","timestamp":1569116969641,"user_tz":240,"elapsed":818,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["avgs.shape\n","dd.shape"],"execution_count":15,"outputs":[{"output_type":"execute_result","data":{"text/plain":["torch.Size([10, 784])"]},"metadata":{"tags":[]},"execution_count":15}]},{"cell_type":"code","metadata":{"id":"FTXQe7ko7ZQV","colab_type":"code","colab":{}},"source":["# batch_size = 10000\n","# all_size = 100000\n","\n","# stats = dict()\n","# for i in range(10):\n","#     stats[i] = 0\n","    \n","    \n","# avgs = torch.zeros(100, 10, 28*28)\n","\n","# for kk in range(100):\n","#   print(kk)\n","  \n","# #   z = torch.rand(all_size, 1, 28, 28)*2 -1 #.cuda()\n","#   z = torch.rand(all_size, 1, 28, 28) #.cuda()\n","  \n","#   z.cuda()\n","#   #plt.imshow(z[1].reshape(28,28))\n","\n","\n","#   all_preds = []\n","#   all_idx = torch.ones(all_size, dtype = torch.uint8)\n","#   for k in range(0,all_size, batch_size):\n","#       y_pred = model(z[k:k+batch_size].cuda())\n","#       #y_pred[y_pred < 0]= 0\n","\n","#       indices = torch.ones(y_pred.size(0), dtype = torch.uint8)\n","#       indices[torch.mean(y_pred, dim =1)==0] = 0 \n","\n","#       all_idx[k:k+batch_size] = indices \n","#       pred = y_pred[indices==1].data.max(1)[1]\n","\n","#       all_preds.append(pred)\n","\n","#   pred = torch.cat(all_preds)\n","\n","#   for i in range(10):\n","#     stats[i] += torch.sum(pred==i)\n","  \n","#   z = z[all_idx]\n","#   for i in range(10):\n","#       a = torch.mean(z[pred==i] , dim=0) \n","#       avgs[kk, i] = a.reshape(28*28)\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"J5Z4VfN0QpBL","colab_type":"code","colab":{}},"source":["z = torch.rand(1000000, 784)\n","grand_mean = torch.mean(z,dim=0)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"tNu7VTILZxE1","colab_type":"code","outputId":"eaeb867c-5366-4d4f-8470-3e26a357ee2e","executionInfo":{"status":"error","timestamp":1565914618689,"user_tz":240,"elapsed":194,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":164}},"source":["# torch.nn.functional.softmax(a)\n","# d = defaultdict()"],"execution_count":0,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-27-e5bb303ade43>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0md\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdefaultdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mNameError\u001b[0m: name 'defaultdict' is not defined"]}]},{"cell_type":"code","metadata":{"id":"P_qVlUIwZwf9","colab_type":"code","outputId":"5a3ce0c1-a746-446f-e929-3ea4d071650f","executionInfo":{"status":"ok","timestamp":1569118129808,"user_tz":240,"elapsed":922,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":187}},"source":["stats"],"execution_count":46,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{0: tensor(80592, device='cuda:0'),\n"," 1: tensor(86779, device='cuda:0'),\n"," 2: tensor(931553, device='cuda:0'),\n"," 3: tensor(23545, device='cuda:0'),\n"," 4: tensor(440971, device='cuda:0'),\n"," 5: tensor(91221, device='cuda:0'),\n"," 6: tensor(4989002, device='cuda:0'),\n"," 7: tensor(439, device='cuda:0'),\n"," 8: tensor(3286916, device='cuda:0'),\n"," 9: tensor(68982, device='cuda:0')}"]},"metadata":{"tags":[]},"execution_count":46}]},{"cell_type":"code","metadata":{"id":"dvHCYz6xRALW","colab_type":"code","outputId":"e3ac3c23-d760-4ef4-b991-f856eb775b0d","executionInfo":{"status":"ok","timestamp":1569118169068,"user_tz":240,"elapsed":999,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":187}},"source":["# grand_mean.shape\n","import sklearn\n","from sklearn import metrics\n","sklearn.metrics.confusion_matrix(pred.cpu().numpy(), evaluate_y.cpu().numpy())\n"],"execution_count":48,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[  9,   8,  12,   7,   4,  13,  10,   9,   9,   7],\n","       [  7,   4,   8,   6,  10,   6,   9,   7,  11,  11],\n","       [ 78,  96,  77,  99,  77, 102,  99,  96,  82,  90],\n","       [  4,   2,   4,   0,   1,   2,   1,   7,   2,   1],\n","       [ 61,  44,  33,  52,  45,  40,  33,  44,  36,  38],\n","       [  6,   7,   9,  13,  11,  10,   4,   9,   4,  12],\n","       [502, 505, 518, 491, 513, 479, 491, 499, 509, 516],\n","       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n","       [330, 324, 335, 326, 330, 337, 345, 324, 338, 320],\n","       [  3,  10,   4,   6,   9,  11,   8,   5,   9,   5]])"]},"metadata":{"tags":[]},"execution_count":48}]},{"cell_type":"code","metadata":{"id":"PLTo_R17UUvZ","colab_type":"code","outputId":"32e6fb9d-bdda-476b-980f-887023a06eaa","executionInfo":{"status":"ok","timestamp":1565914262077,"user_tz":240,"elapsed":144,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["b.data.max(1)[1]\n","b\n","a.shape\n","# avgs"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["torch.Size([1, 28, 28])"]},"metadata":{"tags":[]},"execution_count":25}]},{"cell_type":"code","metadata":{"id":"WaJqeovzgriQ","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":187},"outputId":"9c02abec-69c3-4d4f-f54d-12094f9c8db3","executionInfo":{"status":"ok","timestamp":1569118140739,"user_tz":240,"elapsed":917,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}}},"source":["for i in range(10):\n","    print(torch.sum(pred==i))  \n"],"execution_count":47,"outputs":[{"output_type":"stream","text":["tensor(88, device='cuda:0')\n","tensor(79, device='cuda:0')\n","tensor(896, device='cuda:0')\n","tensor(24, device='cuda:0')\n","tensor(426, device='cuda:0')\n","tensor(85, device='cuda:0')\n","tensor(5023, device='cuda:0')\n","tensor(0, device='cuda:0')\n","tensor(3309, device='cuda:0')\n","tensor(70, device='cuda:0')\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"A6DDnDB784s3","colab_type":"code","outputId":"eef76105-eb3d-4492-ad24-4acb5aef674a","executionInfo":{"status":"ok","timestamp":1566004199257,"user_tz":240,"elapsed":2889,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":[""],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAT0AAAEICAYAAAAtLCODAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n0Vm06+OtEEI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0yOao1PxXPcQLo7fO/y3758Jtcfe0p8fnRADr3x+tg7Wrx6yn97u3/4uqf7r7A1R9c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\nrkJggg==\n","text/plain":["<Figure 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Tq1MxsEXAL0AE4cIO7f87MZgO3A0uBrcBV7h4KrkZHjSODaf1OrFqCUU+v8f3tZ4dl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\nCSEKhYKaEKJQ/H/JQKfmZX9ebwAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/matplotlib/image.py:395: UserWarning: Warning: converting a masked element to nan.\n","  dv = (np.float64(self.norm.vmax) -\n","/usr/local/lib/python3.6/dist-packages/matplotlib/image.py:396: UserWarning: Warning: converting a masked element to nan.\n","  np.float64(self.norm.vmin))\n","/usr/local/lib/python3.6/dist-packages/matplotlib/image.py:403: UserWarning: Warning: converting a masked element to nan.\n","  a_min = np.float64(newmin)\n","/usr/local/lib/python3.6/dist-packages/matplotlib/image.py:408: UserWarning: Warning: converting a masked element to nan.\n","  a_max = np.float64(newmax)\n","/usr/local/lib/python3.6/dist-packages/matplotlib/colors.py:918: UserWarning: Warning: converting a masked element to nan.\n","  dtype = np.min_scalar_type(value)\n","/usr/local/lib/python3.6/dist-packages/numpy/ma/core.py:713: UserWarning: Warning: converting a masked element to nan.\n","  data = np.array(a, copy=False, subok=subok)\n"],"name":"stderr"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAQIAAAEICAYAAAC01Po2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZ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R9X9E5FcbbuseTsaHBuG630rb0p9KN6AxTRKQkJUwRkZKUMEVESlLCFBEpSQlTRKQk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\n","text/plain":["<Figure 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9XAbfV0i8hRHmodeqhVwBvBh40s/vzZdcC7wc+a2ZXA7uBK4o25G0wuSyd8mXRlhNh\n+bbWmaTNHlkRlx2OX5Mv2RWaaR9N73u6L+6BTiyJ5SQbboslIUOPx8d29IXp+9yu284Ky/KSWAoz\ndl+878V7YknIkZel662rIB0U8SlLDHh8n6g9TX07ltm0TBZsuz99XAD7L41TG9l0+px1HonbS+tA\nPP1eM1LToObud5FuXq+upS9CiHKiXxQIIUqFgpoQolQoqAkhSoWCmhCiVCioCSFKhYKaEKJUNO0U\neQDelhYXDe+JU8lEaYu6tg4mbQDje+Pp1oa8YEq0Y2nt0PDaWKe2ZG+cQmfk7L7Q3vt4rN8bWZPW\nXI2tjsVes+OxJmqiL9ZjzbbF9bbl8+nyg5vjfdtUPP3e1OJYqDZ2bFnSNl0wHWPU1gDGV8bHveTh\n+NjGV6Z99wJ9XmuBhq4ZUU9NCFEqFNSEEKVCQU0IUSoU1IQQpUJBTQhRKhTUhBClQkFNCFEqmlen\n1uJ4T6x7ilj0SDqPVMuepWHZ1lWxpml8ZZz/anpRWjzUfqJA07Qs1iy1j8a+Hbw41rFNBCnPZjvi\nbXf0x1qw7iPxsVlcbRy6sDNp6zwW+1Y0Dd1EoPUCaJlM+966ZCosOzMRn7NFj8X20XWxb9NL0hXX\nNhT3Wyx2vSlRT00IUSoU1IQQpUJBTQhRKhTUhBClQkFNCFEqFNSEEKVCQU0IUSqaV6cGMJvWDm07\ne19YdM/DW5K2qZ54t+1Dsd6q81hsHz4rnX9rem2cm2uiL63VAgpvUzPL4gRafavTueSOn4hzvc2O\nFTWn2D65Oj72jmVpsdl4f3zSZjtiTWPbUEE+tkBDNz0U6/Pal8YiuYm+grk3i+YsDQ5txb/GGreB\n7UUbbz7UUxNClAoFNSFEqVBQE0KUCgU1IUSpUFATQpQKBTUhRKlQUBNClIqa6tTMbAPwCWAN4MD1\n7v5hM7sO+CXgSL7qte7+pXBbk0bXvrQ+6IkDaR0awMyatH6nfTDW7oxuKtBTPR1rniLdUc/iWNM0\nEmjzAGwoPqUtBfZjo+l5P9tXj4Vlpw/HGrrJVbFWrOtAwdycw+l69aKW3BEna5vpKri/W7q9tPTG\nScmmj3bF9tVxvXQ8HfvWfSJdL0d+LD5ub411bM1IrcW308C73f0+M1sM3GtmX8ttf+buH6yxP0KI\nklHToObu/UB//nnIzB4B1tfSByFEuanbmJqZbQZeDNydL3qnmT1gZjeY2ZzPQGZ2jZntNLOdM6Mj\nNfJUCNFM1CWomVkv8HngXe4+CHwU2ApsJ+vJ/elc5dz9enff4e47Wnvi3yEKIZ6f1DyomVk7WUD7\npLvfCuDuh9x9xt1ngY8BF9baLyFEOahpUDMzAz4OPOLuH6pYvrZitTcCD9XSLyFEeaj1289XAG8G\nHjSz+/Nl1wJXmtl2MpnHLuAUF3V5AAAEZ0lEQVQdRRvydmd8TSCtKHiF37k/ne5lfE38ir11JL4X\nTG2IZRltbWnfRvf1hmXpLJhHrkBNQqxGwRenj33yRCzZsPZYHtC7Zji0D7fGQwodB9PNtSVWgzC1\nOLZ3Fsgmxs9IV1x7e9xepgpOWVHXom0klvGMbEn71jIWb9wLzlkzUuu3n3cxt0or1KQJIcR80S8K\nhBClQkFNCFEqFNSEEKVCQU0IUSoU1IQQpUJBTQhRKpp3ijw3bDqt32k7HguXJpentUUdR2Ox13RP\ngbbnRLzvjgPp7Y9ujtPYtIzGvvnyuLyNxtOxdT+Vtk/0xYKr2d5YrzUxETe3zqXjoX1yPD0NXuuK\nWBu4pCe2D07E9/fW0bTdgrREAG0FusbZjXFKp1FifWA0Bd/UKZRtVtRTE0KUCgU1IUSpUFATQpQK\nBTUhRKlQUBNClAoFNSFEqVBQE0KUCnNvznxKZnYE2F2xaCUwUCd3ipBvz45G9a1R/YLT79smd191\nGrf3nNO0Qa0aM9vp7jvq7cdcyLdnR6P61qh+QWP7Viv0+CmEKBUKakKIUlGmoHZ9vR0IkG/Pjkb1\nrVH9gsb2rSaUZkxNCCGgXD01IYRQUBNClItSBDUze52ZPWZmT5jZe+rtTyVmtsvMHjSz+81sZ519\nucHMDpvZQxXL+szsa2b2eP5/eYP4dZ2Z7c/r7X4z+5la+5X7scHMvm5mD5vZd8zsN/LljVBvKd8a\nou7qRdOPqZlZK/Bd4LXAPuAe4Ep3f7iujuWY2S5gh7vXXaxpZhcDw8An3P1F+bIPAEfd/f35DWG5\nu/92A/h1HTDs7h+spS9z+LYWWOvu95nZYuBe4HLgrdS/3lK+XUED1F29KENP7ULgCXd/0t0ngU8D\nl9XZp4bE3e8EjlYtvgy4Kf98E9lFUVMSfjUE7t7v7vfln4eAR4D1NEa9pXx7XlOGoLYe2FvxfR+N\ndWId+KqZ3Wtm19TbmTlY4+79+eeDwJp6OlPFO83sgfzxtOaPd9WY2WbgxcDdNFi9VfkGDVZ3taQM\nQa3RucjdXwK8Hvi1/FGrIfFsLKJRxiM+CmwFtgP9wJ/W0xkz6wU+D7zL3QcrbfWutzl8a6i6qzVl\nCGr7gQ0V38/MlzUE7r4//38Y+ALZ43IjcSgfmzk5RnO4zv4A4O6H3H3G3WeBj1HHejOzdrKg8Ul3\nvzVf3BD1NpdvjVR39aAMQe0e4Gwz22JmHcCbgNvr7BMAZrYoH8DFzBYBlwIPxaVqzu3AVfnnq4Db\n6ujLM5wMGDlvpE71ZmYGfBx4xN0/VGGqe72lfGuUuqsXTf/2EyB/Zf0/gFbgBnf/ozq7BICZnUXW\nO4NsOsJP1dM3M7sFuIQsPc0h4L3AF4HPAhvJUjld4e41HbRP+HUJ2eOTA7uAd1SMYdXSt4uAfwIe\nBE7OEXgt2dhVvest5duVNEDd1YtSBDUhhDhJGR4/hRDiGRTUhBClQkFNCFEqFNSEEKVCQU0IUSoU\n1IQQpUJBTQhRKv4/uR0EFwktF1AAAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"CB-oRv2nrGdy","colab_type":"code","colab":{}},"source":["#   \n","# - load the model\n","# - modulate the input\n","# - see how many are classified as the pattern\n","\n","import os\n","\n","model = Model()\n","if cuda:\n","    model.cuda() # CUDA!\n","    \n","model = torch.load(os.path.join(save_path, 'cnn.pth'))\n","\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"44QqDqOZ_Zzp","colab_type":"code","colab":{}},"source":["import torch.nn.functional as F\n","def resize2d(img, size):\n","    return (F.adaptive_avg_pool2d(Variable(img,volatile=True), size)).data\n","\n","  \n","from skimage import io, transform  \n","import sklearn\n","from sklearn import metrics\n","\n","\n","import matplotlib.pyplot as plt\n","import matplotlib.image as mpimg  # for reading image\n","import matplotlib.cm as cm\n","\n","\n","# https://stackoverflow.com/questions/25862026/turn-off-axes-in-subplots/25864515\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"cRuxftdqAR_L","colab_type":"code","outputId":"63f8717b-e6f8-4c53-a3b1-3c2153ada21c","executionInfo":{"status":"ok","timestamp":1566154131775,"user_tz":240,"elapsed":567,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":524}},"source":["z.max()\n","plt.figure()\n","y_pred = model(z.cuda())\n","preds = y_pred.data.max(1)[1].cpu()\n","print(preds)\n","freq = numpy.histogram(preds.numpy(), bins=list(range(11))) \n","y_pos = np.arange(len(freq[0]))\n","plt.bar(y_pos, freq[0]/freq[0].max())\n","plt.xticks(list(range(9)))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["tensor([8, 8, 2, 8, 6, 4, 5, 8, 8, 8, 8, 8, 5, 5, 8, 8, 2, 8, 3, 2, 8, 8, 8, 8,\n","        3, 8, 2, 0, 8, 8, 8, 8, 3, 8, 8, 2, 3, 8, 2, 8, 2, 8, 2, 8, 3, 4, 8, 2,\n","        2, 5, 5, 8, 8, 8, 8, 2, 2, 2, 0, 8, 4, 8, 8, 8, 8, 2, 8, 8, 3, 3, 2, 8,\n","        8, 8, 8, 4, 8, 8, 8, 2, 8, 2, 8, 8, 8, 8, 8, 5, 4, 8, 8, 8, 8, 2, 8, 8,\n","        8, 8, 6, 8])\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["([<matplotlib.axis.XTick at 0x7f90fd192400>,\n","  <matplotlib.axis.XTick at 0x7f90fd192080>,\n","  <matplotlib.axis.XTick at 0x7f90fd192dd8>,\n","  <matplotlib.axis.XTick at 0x7f90f770dbe0>,\n","  <matplotlib.axis.XTick at 0x7f90f770d240>,\n","  <matplotlib.axis.XTick at 0x7f90f770d0b8>,\n","  <matplotlib.axis.XTick at 0x7f90f6bd49b0>,\n","  <matplotlib.axis.XTick at 0x7f90f6bd4518>,\n","  <matplotlib.axis.XTick at 0x7f90f6bd46d8>],\n"," <a list of 9 Text xticklabel objects>)"]},"metadata":{"tags":[]},"execution_count":105},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBhl3SWqQcZekBv0/oaGnb7tHhEwAAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"6Q01F8FtBv2q","colab_type":"code","outputId":"8d26a31b-d874-4b77-f338-a16b12c3787b","executionInfo":{"status":"ok","timestamp":1566154047433,"user_tz":240,"elapsed":248,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["# torch.histc(preds.type(torch.FloatTensor), bins=10)\n","(preds==8).sum()\n","# numpy.coun\n","freq"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(array([ 2,  0, 18,  7,  5,  6,  2,  0, 60]),\n"," array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]))"]},"metadata":{"tags":[]},"execution_count":97}]},{"cell_type":"code","metadata":{"id":"ZglFZOUL1oB4","colab_type":"code","outputId":"fafd12b6-95f4-41a4-f0a9-d296c56a0c9d","executionInfo":{"status":"ok","timestamp":1569119731935,"user_tz":240,"elapsed":4061,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["# load the zero image\n","# objects = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']\n","save_path = 'drive/My Drive/classification_images/fashionMNIST'\n","\n","import os\n","\n","z = torch.rand(10000, 1, 28, 28) #.cuda()\n","weight = 1\n","\n","# frequency of noise predictions\n","f, axarr = plt.subplots(11, 3)\n","# f.set_figheight(1.4)\n","# f.set_figwidth(15)\n","f.set_figheight(15)\n","f.set_figwidth(15)\n","\n","\n","\n","\n","# fixed noise \n","axarr[0,0].imshow(torch.torch.ones_like(torch.squeeze(z[0,...],0)))\n","axarr[0,0].axis('off')\n","\n","\n","# fixed noise \n","axarr[0,1].imshow(torch.squeeze(z[0,...],0))\n","axarr[0,1].axis('off')\n","\n","\n","\n","y_pred = model(z.cuda())\n","preds = y_pred.data.max(1)[1].cpu()\n","# print(preds)\n","freq = numpy.histogram(preds.numpy(), bins=list(range(11))) \n","y_pos = np.arange(len(freq[0]))\n","axarr[0,2].bar(y_pos, freq[0]/freq[0].sum())\n","axarr[0,2].set_yticks([0.5,1]) \n","# axarr[0,2].set_xticks(list(range(9))) \n","# axarr[0,2].set_ylim([0,1])\n","# axarr[0,2].axis('on')\n","\n","\n","\n","# plt.subplots_adjust(hspace=0.01, wspace=0.01)\n","# plt.show()\n","\n","\n","\n","\n","# f, axarr = plt.subplots(10, 3, )\n","# f.set_figheight(15)\n","# f.set_figwidth(15)\n","\n","mis_class = []\n","\n","for i in range(1,11):\n","  # pattern\n","  pattern =  io.imread(os.path.join(save_path, str(i-1) + '-.png'))\n","  pattern = pattern[35:35+217,113:330,:]\n","  pattern = transform.resize(pattern, (28, 28))\n","  pattern = torch.from_numpy(pattern)\n","  pattern = pattern.type(torch.FloatTensor)\n","  pattern = torch.mean(pattern, dim=2)\n","\n","  axarr[i,0].imshow(pattern)\n","  axarr[i,0].axis('off')\n","  \n","  \n","\n","  \n","  # pattern + noise\n","  z_new = (1-weight)*z + weight*pattern #/ (weight+1)  # noise + stim\n","#   z_new = (z_new - z_new.min()) / (z_new.min() - z_new.min())\n","#   print(z_new.min(),z_new.max())\n","  axarr[i,1].imshow(torch.squeeze(z_new[0,...],0))\n","  axarr[i,1].axis('off')\n","\n","  \n","  \n","  \n","  # frequency of pattern + noise predictions  \n","  y_pred = model(z_new.cuda())\n","  preds = y_pred.data.max(1)[1].cpu()\n","#   print(preds)  \n","  freq = numpy.histogram(preds.numpy(), bins=list(range(11))) \n","  y_pos = np.arange(len(freq[0]))\n","  axarr[i,2].bar(y_pos, freq[0]/freq[0].sum())\n","  axarr[i,2].set_xticks(list(range(9))) \n","  axarr[i,2].set_yticks([0.5,1]) \n","\n","  \n","  f.subplots_adjust(hspace=0.06) #, wspace=0.0, right = 0.8)\n","  f.show()\n","  \n","#   axarr[i,2].axis('off')\n","\n","  error = (preds == i-1).sum().type(torch.FloatTensor)/preds.size(0)\n","  print(f'misclassification rate: {error}')\n","  mis_class.append(error)\n","  \n","print(mean(mis_class))"],"execution_count":57,"outputs":[{"output_type":"stream","text":["misclassification rate: 1.0\n","misclassification rate: 1.0\n","misclassification rate: 0.0\n","misclassification rate: 1.0\n","misclassification rate: 1.0\n","misclassification rate: 0.0\n","misclassification rate: 1.0\n","misclassification rate: 0.0\n","misclassification rate: 1.0\n","misclassification rate: 1.0\n","0.7\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAxIAAANSCAYAAADxnPinAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nbn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RCW4oqM5SetBpiNqlhpTma4k9Odi0vH/Oxo5aGfdRTdz26XJJUHcz7WHOvq0ScXFlq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UMTr1kYqa3ZDJG3mahYdjJfNrVTWfOlSuDi+nMr6IbOwke3fXu6xXmRJh4Pz89X1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\n","text/plain":["<Figure size 1080x1080 with 33 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"0nFng6ftQXFF","colab_type":"code","outputId":"d5cb1241-e5bc-4415-a520-b102f87dc686","executionInfo":{"status":"ok","timestamp":1566157677319,"user_tz":240,"elapsed":510,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":286}},"source":["data = data.cpu()\n","plt.imshow(data[5,0])\n","data.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["torch.Size([96, 1, 28, 28])"]},"metadata":{"tags":[]},"execution_count":190},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"NR3maMeMR2d_","colab_type":"code","outputId":"e19512db-82d3-4c56-96a8-9d4a7d766195","executionInfo":{"status":"ok","timestamp":1566158344031,"user_tz":240,"elapsed":318,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["z.shape\n","# z.min().shape\n","# z"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["torch.Size([10000, 1, 28, 28])"]},"metadata":{"tags":[]},"execution_count":210}]},{"cell_type":"code","metadata":{"id":"jJWJtEedP3ix","colab_type":"code","outputId":"d850c15f-fc67-4a54-80fc-6590f60be889","executionInfo":{"status":"ok","timestamp":1568995060323,"user_tz":240,"elapsed":4073,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["\n","save_path = 'drive/My Drive/classification_images/fashionMNIST'\n","\n","import os\n","\n","\n","# z = next(iter(test_loader)) \n","z = test.data # 10K test images\n","z = z[:,None,...]\n","z = z.type(torch.FloatTensor)\n","z = (z - z.min())/(z.max() - z.min())\n","\n","\n","\n","for i in range(1,11):\n","  # pattern\n","  pattern =  io.imread(os.path.join(save_path, str(i-1) + '-.png'))\n","  pattern = pattern[35:35+217,113:330,:]\n","  pattern = transform.resize(pattern, (28, 28))\n","  pattern = torch.from_numpy(pattern)\n","  pattern = pattern.type(torch.FloatTensor)\n","  pattern = torch.mean(pattern, dim=2)\n","\n","  axarr[i,0].imshow(pattern)\n","  axarr[i,0].axis('off')\n","  \n","  \n","  # pattern + noise\n","  z_new = (1-weight)*z + weight*pattern #/ (weight+1)  # noise + stim\n","#   z_new = (z_new - z_new.min()) / (z_new.min() - z_new.min())\n","#   print(z_new.min(),z_new.max())\n","  axarr[i,1].imshow(torch.squeeze(z_new[0,...],0))\n","  axarr[i,1].axis('off')\n","  \n","  \n","  # frequency of pattern + noise predictions  \n","  y_pred = model(z_new.cuda())\n","  preds = y_pred.data.max(1)[1].cpu()\n","#   print(preds)  \n","  freq = numpy.histogram(preds.numpy(), bins=list(range(11))) \n","  y_pos = np.arange(len(freq[0]))\n","  axarr[i,2].bar(y_pos, freq[0]/freq[0].sum())\n","  axarr[i,2].set_xticks(list(range(9))) \n","  axarr[i,2].set_yticks([0.5,1]) \n","  preds \n","  \n","  f.subplots_adjust(hspace=0.06) #, wspace=0.0, right = 0.8)\n","  f.show()\n","  \n","  \n","  # targetted attack!\n","  error = (preds == i-1).sum().type(torch.FloatTensor)/preds.size(0)\n","  # untargetted attack!\n","  # error = (preds != test.targets).sum().type(torch.FloatTensor)/preds.size(0)\n","\n","  print(f'misclassification rate: {error}')\n","  mis_class.append(error)\n","  \n","print(mean(mis_class))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["misclassification rate: 0.016899999231100082\n","misclassification rate: 0.013899999670684338\n","misclassification rate: 0.012900000438094139\n","misclassification rate: 0.019600000232458115\n","misclassification rate: 0.01889999955892563\n","misclassification rate: 0.016699999570846558\n","misclassification rate: 0.017799999564886093\n","misclassification rate: 0.019999999552965164\n","misclassification rate: 0.01860000006854534\n","misclassification rate: 0.019600000232458115\n","0.017490001\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAxIAAANSCAYAAADxnPinAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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rk3Sdrjvb9F0mudcy+V1Jb0kKTrV23AAMbCaFObTNpRTPnp2lSBEJHv2MVGQlpR3ewT4bqF9KTw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\nqbVW/1SIPvhjpbB67lQsoraRklSnaOoxa1GHvHEt9YzA9lWLP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H+G3fL54y8L49jedskH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\nAAbGgwQAAACAgf1/p7gk5KoADM4AAAAASUVORK5CYII=\n","text/plain":["<Figure size 1080x1080 with 33 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"rWE-ZmZElKES","colab_type":"code","outputId":"b6abe16d-d3b3-494c-da9b-aa8526fa02c8","executionInfo":{"status":"ok","timestamp":1568994826135,"user_tz":240,"elapsed":827,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# mean(mis_class)\n","test.targets"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["tensor([7, 2, 1,  ..., 4, 5, 6])"]},"metadata":{"tags":[]},"execution_count":39}]},{"cell_type":"code","metadata":{"id":"thdzCfpSzR4e","colab_type":"code","outputId":"85cb94e7-a4d3-406d-c4b5-ba7ddd4131e0","executionInfo":{"status":"error","timestamp":1566156691792,"user_tz":240,"elapsed":3911,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":334}},"source":["pattern =  io.imread(os.path.join(save_path, str(i) + '-.png'))\n","pattern = transform.resize(pattern, (28, 28))\n","pattern.shape\n","pattern = torch.mean(pattern, dim=2)\n","\n","\n","https://stackoverflow.com/questions/19626530/python-xticks-in-subplots"],"execution_count":0,"outputs":[{"output_type":"error","ename":"TypeError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-165-84697c1801c2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mpattern\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtransform\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpattern\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m28\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m28\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mpattern\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mpattern\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpattern\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mTypeError\u001b[0m: mean() received an invalid combination of arguments - got (numpy.ndarray, dim=int), but expected one of:\n * (Tensor input)\n * (Tensor input, torch.dtype dtype)\n      didn't match because some of the keywords were incorrect: dim\n * (Tensor input, tuple of ints dim, torch.dtype dtype, Tensor out)\n * (Tensor input, tuple of ints dim, bool keepdim, torch.dtype dtype, Tensor out)\n * (Tensor input, tuple of ints dim, bool keepdim, Tensor out)\n"]}]},{"cell_type":"code","metadata":{"id":"gm0-JahuWkDC","colab_type":"code","outputId":"72aef081-e3ad-4224-f314-ab2c65171d45","executionInfo":{"status":"ok","timestamp":1566162691135,"user_tz":240,"elapsed":296,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# digits = next(iter(train_loader))\n","\n","# 1326/2000"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["tensor(0.1326)"]},"metadata":{"tags":[]},"execution_count":266}]},{"cell_type":"code","metadata":{"id":"94JurDSAW0EW","colab_type":"code","colab":{}},"source":["from torchvision import transforms, datasets\n","def mnist_data():\n","    compose = transforms.Compose(\n","        [transforms.ToTensor(),\n","         transforms.Normalize((.5), (.5))         \n","#          transforms.Normalize((.5, .5, .5), (.5, .5, .5))\n","        ])\n","    transform = transforms.Compose([\n","        transforms.ToTensor(), transforms.Normalize([0.5], [0.5])])    \n","    \n","    out_dir = './dataset'\n","    return datasets.MNIST(root=out_dir, train=True, transform=transform, download=True)\n","\n","\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"H3lSioNEYxqu","colab_type":"code","outputId":"3df31623-bb3c-473c-a52f-37080f4d83cf","executionInfo":{"status":"error","timestamp":1564951730148,"user_tz":240,"elapsed":203,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":164}},"source":["train_loader"],"execution_count":0,"outputs":[{"output_type":"error","ename":"AttributeError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)","\u001b[0;32m<ipython-input-222-b60042ec47b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain_loader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mAttributeError\u001b[0m: 'DataLoader' object has no attribute 'data'"]}]},{"cell_type":"code","metadata":{"id":"vq-33xNK0dSw","colab_type":"code","outputId":"cc57de87-93f0-4d1b-a802-0b6ea5b446fd","executionInfo":{"status":"ok","timestamp":1564952889960,"user_tz":240,"elapsed":5556,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["# ls 'drive/My Drive/classification_images'\n","# torch.squeeze(z[0,...],0).shape\n","# plt.hist(y_pred.data.max(1)[1].cpu())\n","\n","objects = ('0', '1', '2', '3', '4', '5', '6', '7', '8', '9')\n","\n","# evaluate_x = Variable(test_loader.dataset.test_data.type_as(torch.FloatTensor()))\n","# evaluate_y = Variable(test_loader.dataset.test_labels)\n","# if cuda:\n","#     evaluate_x, evaluate_y = evaluate_x.cuda(), evaluate_y.cuda()\n","\n","\n","#digits = next(iter(train_loader))\n","\n","\n","\n","# Load data\n","data = mnist_data()\n","\n","tt = data.targets[data.targets==9]\n","dd = data.data[data.targets==9] \n","data.targets = tt\n","data.data = dd\n","data_loader = torch.utils.data.DataLoader(data, batch_size=100, shuffle=True, drop_last =True)\n","# Num batches\n","\n","z = next(iter(data_loader)) \n","\n","\n","for i in range(10):\n","\n","  pattern =  io.imread(os.path.join(save_path, str(i) + '-.png'))\n","  pattern = pattern[35:35+217,113:330,0]\n","  pattern = transform.resize(pattern, (28, 28))\n","  # pattern = torch.from_numpy(pattern)\n","\n","  pattern = torch.from_numpy(pattern)\n","  pattern = pattern.type(torch.FloatTensor)\n","\n","  plt.figure()\n","  plt.subplot(151)\n","  plt.imshow(pattern)\n","\n","  z_old = (z[0] + 1)/2\n","  \n","  plt.subplot(152)\n","  plt.imshow(torch.squeeze(z_old[0,...],0))\n","\n","  y_pred = model(z_old.cuda())\n","  print(y_pred.data.max(1)[1])\n","  \n","  \n","  plt.subplot(153)\n","  freq = y_pred.data.max(1)[1].cpu()\n","  aa = numpy.histogram(freq.numpy()) \n","  y_pos = np.arange(len(aa[0]))\n","  plt.bar(y_pos, aa[0]/aa[0].max())\n","  plt.xticks(y_pos, objects)\n","\n","  \n","  z_new = .3*z_old + pattern\n","\n","  plt.subplot(154)\n","  plt.imshow(torch.squeeze(z_new[0,...],0))\n","\n","  y_pred = model(z_new.cuda())\n","#   print(y_pred.data.max(1)[1])\n","  \n","  plt.subplot(155)\n","  freq = y_pred.data.max(1)[1].cpu()\n","  aa = numpy.histogram(freq.numpy()) \n","  y_pos = np.arange(len(aa[0]))\n","  plt.bar(y_pos, aa[0]/aa[0].max())\n","  plt.xticks(y_pos, objects)\n","  print(y_pred.data.max(1)[1])\n","#   plt.bar()\n","#   plt.hist(y_pred.data.max(1)[1].cpu())"],"execution_count":0,"outputs":[{"output_type":"stream","text":["tensor([9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n","        9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n","        9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n","        9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n","        9, 9, 9, 9], device='cuda:0')\n","tensor([0, 0, 0, 0, 0, 0, 0, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","        9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 7, 0, 0, 0, 0, 0, 0,\n","        0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 9, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 0, 0,\n","        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","        0, 0, 0, 0], device='cuda:0')\n","tensor([9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n","        9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n","        9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n","        9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n","        9, 9, 9, 9], device='cuda:0')\n","tensor([1, 1, 2, 2, 1, 1, 1, 2, 1, 1, 1, 1, 5, 1, 1, 2, 2, 1, 2, 1, 2, 1, 2, 1,\n","        2, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 2, 1, 1, 1,\n","        1, 1, 1, 1, 1, 2, 2, 2, 7, 1, 2, 2, 1, 2, 2, 1, 1, 2, 1, 2, 1, 2, 1, 1,\n","        1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1,\n","        1, 1, 1, 2], device='cuda:0')\n","tensor([9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n","        9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n","        9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n","        9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n","        9, 9, 9, 9], device='cuda:0')\n","tensor([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n","        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n","        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n","        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n","        2, 2, 2, 2], device='cuda:0')\n","tensor([9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n","        9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n","        9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n","        9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n","        9, 9, 9, 9], device='cuda:0')\n","tensor([3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n","        9, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n","        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 9, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n","        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n","        3, 3, 3, 3], device='cuda:0')\n","tensor([9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 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9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n","        9, 9, 9, 9], device='cuda:0')\n","tensor([5, 5, 9, 7, 5, 5, 5, 7, 5, 9, 7, 7, 5, 9, 5, 7, 7, 7, 7, 9, 5, 9, 7, 9,\n","        9, 7, 5, 5, 5, 9, 5, 5, 7, 5, 9, 5, 5, 5, 5, 5, 9, 7, 7, 5, 7, 5, 5, 5,\n","        9, 5, 5, 5, 5, 7, 7, 5, 7, 9, 7, 7, 5, 9, 9, 7, 5, 9, 7, 9, 9, 7, 7, 5,\n","        7, 9, 7, 5, 5, 5, 5, 9, 7, 5, 5, 7, 5, 5, 5, 5, 5, 5, 2, 7, 5, 5, 5, 5,\n","        9, 5, 5, 7], device='cuda:0')\n","tensor([9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n","        9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n","        9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n","        9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n","        9, 9, 9, 9], device='cuda:0')\n","tensor([0, 6, 0, 0, 6, 6, 6, 4, 6, 6, 6, 6, 0, 0, 9, 0, 6, 6, 6, 9, 6, 6, 6, 6,\n","        9, 0, 6, 3, 6, 0, 6, 6, 0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 6, 6, 6, 6,\n","        6, 6, 6, 6, 6, 0, 6, 6, 0, 6, 0, 6, 6, 6, 9, 6, 0, 6, 6, 0, 6, 6, 6, 6,\n","        6, 6, 0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,\n","        0, 6, 6, 0], device='cuda:0')\n","tensor([9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n","        9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n","        9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n","        9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n","        9, 9, 9, 9], device='cuda:0')\n","tensor([7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,\n","        7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,\n","        7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,\n","        7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,\n","        7, 7, 7, 7], device='cuda:0')\n","tensor([9, 9, 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device='cuda:0')\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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5mtiPtXAHtmpopzEy+V2Pe3n2beFZfQue5iAJoWdFE8FDpbT8VnIsZndHSU6667jte97nW8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\nVuAnwG8BlwGP1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size 432x288 with 5 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size 432x288 with 5 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DPUgorlsIU5HPdFFetorJnhPGtj3PWO/8dR7/4lZN7J/NEfiCMI9ywftu0558th97K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\nd78HuKfl2G81fH5t2y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8BZGuLulzoc2VWFrqZtRKU+Wfd/Stx9y4zWxuPrwWeXZgmLk28WuHJm29gxWnns/z0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\nzH5iZvfE/f+t7pySmd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bFpcdImljfPF1rnBABsLE/ZLht7wsWRnkonmdBXWTNgjT/Ypq1PF6zOMb22z/J4tde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\n","text/plain":["<Figure size 432x288 with 5 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"sAP_1AyrIJQp","colab_type":"code","outputId":"e0fdbdc1-af5a-49c1-9a31-9eccad9781d4","executionInfo":{"status":"ok","timestamp":1564951868791,"user_tz":240,"elapsed":197,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["  z.min()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["tensor(0.)"]},"metadata":{"tags":[]},"execution_count":232}]},{"cell_type":"code","metadata":{"id":"REXMt5SvgdbK","colab_type":"code","outputId":"cf279773-9d32-4b57-dc0a-3c8f0d207efc","executionInfo":{"status":"ok","timestamp":1564869780908,"user_tz":240,"elapsed":313,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":187}},"source":["for i in range(10):\n","    print(torch.sum(pred==i))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["tensor(236, device='cuda:0')\n","tensor(3, device='cuda:0')\n","tensor(1874, device='cuda:0')\n","tensor(160, device='cuda:0')\n","tensor(2689, device='cuda:0')\n","tensor(22, device='cuda:0')\n","tensor(44, device='cuda:0')\n","tensor(81, device='cuda:0')\n","tensor(93796, device='cuda:0')\n","tensor(98, device='cuda:0')\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"DQa2gDav9kKr","colab_type":"code","outputId":"db3940f6-bccd-4b68-e5d1-990a3e0776e4","executionInfo":{"status":"error","timestamp":1564867442930,"user_tz":240,"elapsed":288,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":300}},"source":["# ls\n","from google.colab import files\n","files.download('./*.png')\n"],"execution_count":0,"outputs":[{"output_type":"error","ename":"FileNotFoundError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-96-9eb9ebc38cb1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mgoogle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolab\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mfiles\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mfiles\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdownload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'./*.png'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/google/colab/files.py\u001b[0m in \u001b[0;36mdownload\u001b[0;34m(filename)\u001b[0m\n\u001b[1;32m    142\u001b[0m       \u001b[0;32mraise\u001b[0m \u001b[0mOSError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    143\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 144\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0mFileNotFoundError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=undefined-variable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    145\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    146\u001b[0m   \u001b[0mstarted\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_threading\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mEvent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mFileNotFoundError\u001b[0m: Cannot find file: ./*.png"]}]},{"cell_type":"code","metadata":{"id":"SBxJ8w3AYAbz","colab_type":"code","colab":{}},"source":["ls drive/My\\ Drive/classification_images\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"3GAnOj4gXjd8","colab_type":"code","outputId":"8b431de1-784f-4821-caca-18c31191ec3f","executionInfo":{"status":"ok","timestamp":1564867529154,"user_tz":240,"elapsed":24357,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":122}},"source":[""],"execution_count":0,"outputs":[{"output_type":"stream","text":["Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n","\n","Enter your authorization code:\n","··········\n","Mounted at /content/drive/\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"x1TYqjT17ejE","colab_type":"code","outputId":"ce93940e-d31e-48b7-cdee-26778485fc75","executionInfo":{"status":"ok","timestamp":1564860495533,"user_tz":240,"elapsed":2235,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["# z = z[all_idx]\n","pred = torch.cat(all_preds)\n","for i in range(10):\n","    plt.figure()\n","    a = torch.mean(z[pred==i] , dim=0) \n","    a = a.view(-1,28)\n","    b = model(a[None,None,...].cuda())\n","    c = b.data.max(1)[1]\n","    plt.title(f'gt: {str(i)}  -  pred: {str(c.cpu().data[0].numpy())} -  size-gt: {z[pred==i].size(0)}')\n","    plt.imshow(1-a)"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAP8AAAEICAYAAACQ6CLfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJztnXt4VOW59u8n55AECInkwPnkARBB\nEVDEgvWsLWJbqnV7afUr7a5u2+rXfn7d7VW/ax/qtlut2t3ujUrV1rZbW92iVSuiApYNEpGjHMUA\nCSEcEiAJIYeZ5/tjVtxDzPvMEJKZkff+Xddcmcy93rXetWbdM2vW8z7PK6oKQoh/pCW7A4SQ5EDz\nE+IpND8hnkLzE+IpND8hnkLzE+IpNL9niIiKyOhk98NCRDaKyMxk9+NUxyvzi8hMEak6wTYiIv8i\nIgeDx7+IiPRWH1MJEckWkQUickRE9orI3YnYrqqOU9V3ErGtDkSkUkQuPYHls0Tkj0E7dX1YBctt\nOtHzLhF4Zf5uMg/AdQDOATABwBcAfDOpPQoQkfRe3sR9AMYAGAZgFoAfiMiVvbzNzxLvAvgbAHuN\nZb4PYH9iunOCqOop9QBwLoAPADQAeB7AfwL4RwB5AJoBhAE0Bo/yONa3HMC8qP9vB7Cil/o+E0AV\ngB8COACgEsBNUfpTAH4F4FUATQAuBZAN4F8B7AJQC+DfAeRGtfk+gBoAewDcBkABjI6zP3sAXB71\n/z8A+EMP7WsxgFcAHAJQB2AZgLRAqwRwafD8UNT71RT0f3igXQtgTbDMcgATjO3lAngaQD2ATQB+\nAKAq0H4TnBfNwXZ+cIL7UgVgZhevjwi2dVXHtlLpkfQO9OjOAFkAdgL4DoBMANcDaAXwj4E+s/Ob\nAOAiAIeMdR4GMDXq/8kAGnqp/zMBtAN4KDD154IT/oxAfyroz3RErtpyADwMYCGAAQAKALwM4KfB\n8lcGHwjjEfnw+120+QF8DcA6R18Kg2VLol77MoD1PbSvPw0+qDKDxwwAEmifmL9Tm38GsDRYfhKA\nfQCmAkgHcEvQLtuxvfsBLAn2azCAddHnQlfbDJb5Whz74jL/KwDmdHXepcLjVLvsnwYgA8Cjqtqm\nqi8AeM9qoKrvqmp/Y5F8RAzXwWEA+b38u//HqtqiqksA/BnA3CjtJVX9q6qGAbQg8rPke6pap6oN\niBjkhmDZuQB+raobVLUJkcv4T1DV36nqBEcf8oO/nfe94GR2LIo2AGUAhgXv1TINHNMVIvJVRD6s\nvqSqbYjs93+o6kpVDanq04gcj2mOVcwF8M+qWq+qVQAejdVBVZ2gqr87wf3q6O8cAOmq+mJ32ieC\nU8385QCqO51Eu09ynY0A+kb93xdAo3WidhDctW4MHjPi3F59YNQOdiKyXx1E789pAPoAeF9EDonI\nIQCvB68jaBe9/M44+wBE9hv49L43dLVwN/b1ZwC2A3hDRHaIyL2uBUVkEoBfAJijqh2/n4cBuKdj\nv4N9HwKgXERuiurLa8HynY/FyZ4XTkQkD8ADAO7qrW30BBnJ7kAPUwNgkIhIlDmHAPgoeN6dFMaN\niNzs67iCOCd4LSaqOq4b2ysUkbyoD4ChADZErzbq+QFEfqeOU9XqLtZVg8j+dzA03k6oar2I1CCy\nv4uCl537fqL7Glyl3IOIgccDeEtEVqnq4ujlRGQggP8CcIeqfhAl7QbwT6r6T45NPNvp/xpELvc/\nDP4f0knvyfTWMQCGA1gWXCBmAegnInsBTFPVyh7cVrc51b75/xtACMCdIpIhIrMBTInSawEUiUi/\nE1jnMwDuFpFBIlKOyAn7VE912MH/C0JEMxC5qfV8VwsFl/6PA3g4MAmCfl4RLPIcgFtFZKyI9AHw\nkxPsxzMAfiQihSJyJoBvoIf2XUSuFZHRwc+nw4i8b+FOy2QA+COA36rqc51W8TiAb4nI1CAcmyci\n14iI62fJcwD+b7AvgwDc2UmvBTDyBPchW0Rygn+zRCQn2J8NiHy4TAwe/ytY/0T04hXHCZPsmw49\n/UDkhtwaRC5bnwfwAiK/oTv0BQAOInKHuByRG02NxvoEkUu4uuDxAIIbU73Q95mI3Dz6e0S+1XcB\nuDlKfwrBzcuo13IQ+Z2/A8ARRO4u3xWl34tIKOpTd/sB3ARgo9Gf7OB4HUHk5L27B/f1e4jcZGsK\n9jn6PapEJJIxPOhvE/7njn8jgKHBclcCWBW8lzXB+13g2F4eInf1DwXH6EcAPorSZwfH+xCA/x28\nthFR0ZYu1lkZ9C/6Mdz1vibbG50fHXdXT1lEZCWAf1fVXye7L7EIBor8VlUHJ7svpzoi8rcAblDV\nzyW7L8niVLvsh4h8TkRKg8v+WxAZmPN6svtFkouIlInIdBFJE5EzEPn5lrJ34hPBqXbDDwDOQOT3\nXR4il8JfVtWa5HaJpABZAP4DkYE3hwD8AcAvk9qjJHPKX/YTQrrmlLvsJ4TER0Iv+9Pz8jSzcIBT\nL+h71Gzf0JLj1NIa7M+xUJ59hZOd1WbqbSF3Dk04ZG87IzNk6uEG+20oLjpi6vsa3YPusrPt/Wpp\nzjL144NvnyY9t91ufrT7p5jkxjhurTHymoy+Dy6sM5vub8039dYG+7il2V1HKNd9Pkp79wePttXX\nIdTUFNcKTsr8QYbXI4iMrX5CVe+3ls8sHIDBd37Pqc+6dI25vSWV7jT0vMX2m1V/Qaupnz7USswC\nqg+7hwY0Hc4125aUHDL1hndKTP22m+37lY+tuMSpnTHSvt2xdaMdWEg/an+w9R170NSPvl/s1MLp\n9gdyxlj7Q+/YbnukcVqL2wP3z+k8Buh45u++2NR3LbHHS2XXmzIOn+3+UM48GMOWxmGr+sXDdtso\nun3ZH6ST/hsiGUtjAdwoImO7uz5CSGI5md/8UwBsV9UdqtqKyN3T2T3TLUJIb3My5h+E44cqVgWv\nHYeIzBORChGpCDU1dZYJIUmi1+/2q+p8VZ2sqpPT8/J6e3OEkDg5GfNX4/jMqMHBa4SQzwAnY/5V\nAMaIyAgRyUKkgMTCnukWIaS36XaoT1XbReROAH9BJNS3QFXNPPe8gmZMm+VeJDvNjhlPH/qxU1tR\n5CpIE+Ha8etM/bWtdjq67nGPMcgd0WV9i0+o/cgd7gKAnExTxmPv2kVlC0rd29+yo8xeeYxwm8SI\n8x/ZVGS3z3avf+KMrWbbD5afbur5e+xw9pGx7nDawZAdGt66yw6/njbVrsl5oNI9ngWww3ntBfZB\nzy1rdGqSE2OAQRQnFedX1VcRKSZJCPmMweG9hHgKzU+Ip9D8hHgKzU+Ip9D8hHgKzU+IpyQ0n7+x\nMRfLl7sT/3JixMvL+rlTPM/7wganBgCvLZ5s6hkj3bFTAMgZ407LLc63cxY+asw29bSqGHnpMWLx\nR7e5JxzKHGzXSJhwhj0o8/2NdjXrGedsNvU1tZ9K9/iEVTHWnb/fjuNf//V3TD0nzR3nf3TTLLNt\nRq2dr1+/3x67kT642dRhpIEXj7BrDdSvN9Kkj8U/dyu/+QnxFJqfEE+h+QnxFJqfEE+h+QnxFJqf\nEE9JaKgvrR3I2e/+vJFae/Lc7cPdlYA+anOHlABg8ES7Ou+edaWmfvUlK53aW1V26mn/QjsUmDHD\nDnGmvXWaqeddWuvUJhTtMdsueW2Sqd963Tum/tTyi0z9kkkfOrWV4WFm21CRHep7+t0Zpp5W2OLW\ndtkVlzXTDq+GY+jlA+zKw3uNFPE0sdc9/Pwqp7Y/z65Sfdx24l6SEHJKQfMT4ik0PyGeQvMT4ik0\nPyGeQvMT4ik0PyGektA4P/JCkKnu1NiiPDv9tHWVuwx1+9BjZtuq2kJTzxpmp/S+8upUp3bOLLsE\n9ar1o0w9VsruNTe8b+qT8nc6tZ+uvspsO+0ydxweALY02iWsy0ccMPW333eXRF/xxYfMtjNXftPU\nB46wZwiub+jj1Aon2KW3m1rslN4+L9hjUvYW2fp3rnrNqT22bqbZtrTcPS4k1hiB45aNe0lCyCkF\nzU+Ip9D8hHgKzU+Ip9D8hHgKzU+Ip9D8hHhKQuP84bCg+ai7jPXBv7pLUANAqMw9dbGG7dzvnK3u\n/GkACPWxy2u39XXHT88qsGsFVOTZeeu/vPBZU7/7qdtNfcPF7vEPabvs/V4pw01da+z2WfX290fh\nVPc4gAuX3WG2PXfoblNfW23XcAiF3H0Lhe1+W+cpALTaJRwg1fZxe7T6aqd2xzXuMQAA8Miyy51a\nc7M9PiGakzK/iFQCaAAQAtCuqnZxfEJIytAT3/yzVNUe5kUISTn4m58QTzlZ8yuAN0TkfRGZ19UC\nIjJPRCpEpCLUYNeyI4QkjpO97L9IVatFZCCARSKyWVWXRi+gqvMBzAeA7JGD4s86IIT0Kif1za+q\n1cHffQBeBDClJzpFCOl9um1+EckTkYKO5wAuB2BPlUsISRlO5rK/BMCLItKxnt+p6uvmxjLCOM2o\nZ35shj2tca4Rm81MD5lt6wfZu5rRYE9tPPhMd2383663L3hmnW7n+9/1x9tM/dLZq039ja1nObXQ\ngHazbdYOu3791M9vNPXVL4039aPH3PHykhi17SvWjjb1omH1pt7+qnsq62OX2fXtQ832+RAe6p4T\nAAAyqu1xAiOmuscwPLboSrNtVpm77oVkxP/LutvmV9UdAM7pbntCSHJhqI8QT6H5CfEUmp8QT6H5\nCfEUmp8QT0loSq+q4FhrplM/0mCHnXLXu/U2O9IHjLZDXrE4+I47bVYG2OGVt9vP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PiKTQ/IZ5C8xPiKTQ/IZ4SNW+/iOQDeBhALgAFsERV7xGROwB8A8Ce8KO3qeor1rLq\n69Kx7kN3LP+LZ9t53J/5wJ0fv++6NLNt7Sw7Jjwxr8LUi5a5+73/LHfMFgAadtmx8rax9nz+2hY7\nbDt2RKVT21Fm1wzIzLDj/AfskDM0yuXjo4phTq1uvx0Mz2m2xzfkPZlq6k193PP9Z9282mxbUufO\nBQAAaafbtRRG97ZzODzz2llOLfWQfbwbit1jFLQpygGLIJaiHS0AvqeqH4hIHwDvi8gboXa3qv4i\n5rURQnoMUc2vquUAysO/q0VkI4Dh3d0xQkj3cly/+UVkJIAZAI7cn39bRNaKyAMi0t/RZrGIFIpI\nYWtNbac6SwjpOmI2v4hkAXgWwHdV9TCA3wIYA2A6gjuDX7bXTlWXqGqBqhYkZ2V2QZcJIV1BTOYX\nkVQExn9UVZ8DAFWtVNVWVW0D8HsAs7uvm4SQriaq+UVEAPwRwEZVvSvi/aERH1sAYH3Xd48Q0l3E\n8rT/LABfBbBORNaE790G4CoRmY4g/FcE4JvRFpSa0YzhE91hqefeOc1sL2nu0I9G2ZLsvnZ+7epf\n5Jt6y2z3umvL7Cm5ue+bMvbNt0N9mSvtkNi25CFuMckOl7W9aE+FltGmjLoRdt+lzJg6m2r3rbG/\nHfJqTbcPeqOxaS8V2mXRo5V077/evm6ummGXfJ979gantuU3k8y2KXXudSe5Z55/ejnRPqCqK4B2\nJ16bMX1CSM+GI/wI8RSanxBPofkJ8RSanxBPofkJ8RSanxBPiSXO32VkJLdgQr8qpz7uzD1ODQCW\n73DHTutr7PLeGkU/5V/dKaYBoHjVZKeWXmFPLa3+4iFTR6Pdvn6IHQ9PqnVP40xqtuPV+dfsMPXN\nVXZ58KRNdgrsrOn7nNrhjfYYg/Hzt5r6R7vzTD19k/uYjx1Xbrata7aPSdYj9lD1/afa59t7r09x\nam3z7Tkw2a+7x30k2TONj/5s7B8lhJxI0PyEeArNT4in0PyEeArNT4in0PyEeArNT4inxLVEt4js\nAbAr4q2BAOwcx4mjp/atp/YLYN86Slf2bYSqDorlg3E1/6dWLlKoqgUJ64BBT+1bT+0XwL51lET1\njbf9hHgKzU+IpyTa/EsSvH6Lntq3ntovgH3rKAnpW0J/8xNCEkeir/yEkARB8xPiKQkxv4h8QUQ2\ni8g2Ebk1EX1wISJFIrJORNaISGGC+/KAiFSJyPqI93JE5A0R2Rr+226NxAT17Q4RKQ333RoRuThB\nfcsXkbdF5GMR2SAiN4XvJ3TfGf1KyH6L+29+EUkGsAXA+QBKAKwGcJWqfhzXjjgQkSIABaqa8AEh\nIvI5ADUAHlbVKeF7PwewX1V/Fn5x9lfVH/SQvt0BoCbRZdvDalJDI8vKA7gCwLVI4L4z+rUQCdhv\nibjyzwawTVV3qGoTgCcAXJ4Aw2acAAABgUlEQVSAfvR4VHU5gP3HvH05gKXh30sRnDxxx9G3HoGq\nlqvqB+Hf1QCOlJVP6L4z+pUQEmH+4QCKI/5fggTugHZQAK+LyPsisjjRnWmHXFU9koOqAkBuIjvT\nDlHLtseTY8rK95h915Fy910NH/h9mjmqeiqAiwDcEN7e9kg0+M3Wk2K1MZVtjxftlJX/hETuu46W\nu+9qEmH+UgCRVTHzwvd6BKpaGv5bBeB59LzS45VHKiSH/7ozosaZnlS2vb2y8ugB+64nlbtPhPlX\nAxgnIqNEJA3AlQBeTEA/PoWIZIYPYiAimQAuQM8rPf4igEXh34sA/CmBfTmKnlK23VVWHgnedz2u\n3L2qxv0F4GIET/y3A/hRIvrg6NdoAB+Frw2J7huAxxHcBjYjeDZyHYABAN4CsBXAmwByelDf/h+A\ndQDWIjDa0AT1bQ6CW/q1ANaEr4sTve+MfiVkv3F4LyGewgd+hHgKzU+Ip9D8hHgKzU+Ip9D8hHgK\nzU+Ip9D8hHjK/wcNT6gLuPN+1gAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"vs1QSHq79WhR","colab_type":"code","colab":{}},"source":["# for i in range(z.size(0)):\n","#   print(torch.max(data[1,0]))"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"5d6-r9IX8wz2","colab_type":"code","outputId":"ce2c98f0-bdba-411e-be73-a539a19e255e","executionInfo":{"status":"ok","timestamp":1565041850498,"user_tz":240,"elapsed":12876,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":901}},"source":["# Repeating the above experiment with VGG and ResNet \n","# VGG16\n","\n","# https://towardsdatascience.com/model-summary-in-pytorch-b5a1e4b64d25\n","\n","\n","import torch\n","from torchvision import models\n","from torchsummary import summary\n","device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n","vgg = models.vgg16().to(device)\n","summary(vgg, (3, 224, 224))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["----------------------------------------------------------------\n","        Layer (type)               Output Shape         Param #\n","================================================================\n","            Conv2d-1         [-1, 64, 224, 224]           1,792\n","              ReLU-2         [-1, 64, 224, 224]               0\n","            Conv2d-3         [-1, 64, 224, 224]          36,928\n","              ReLU-4         [-1, 64, 224, 224]               0\n","         MaxPool2d-5         [-1, 64, 112, 112]               0\n","            Conv2d-6        [-1, 128, 112, 112]          73,856\n","              ReLU-7        [-1, 128, 112, 112]               0\n","            Conv2d-8        [-1, 128, 112, 112]         147,584\n","              ReLU-9        [-1, 128, 112, 112]               0\n","        MaxPool2d-10          [-1, 128, 56, 56]               0\n","           Conv2d-11          [-1, 256, 56, 56]         295,168\n","             ReLU-12          [-1, 256, 56, 56]               0\n","           Conv2d-13          [-1, 256, 56, 56]         590,080\n","             ReLU-14          [-1, 256, 56, 56]               0\n","           Conv2d-15          [-1, 256, 56, 56]         590,080\n","             ReLU-16          [-1, 256, 56, 56]               0\n","        MaxPool2d-17          [-1, 256, 28, 28]               0\n","           Conv2d-18          [-1, 512, 28, 28]       1,180,160\n","             ReLU-19          [-1, 512, 28, 28]               0\n","           Conv2d-20          [-1, 512, 28, 28]       2,359,808\n","             ReLU-21          [-1, 512, 28, 28]               0\n","           Conv2d-22          [-1, 512, 28, 28]       2,359,808\n","             ReLU-23          [-1, 512, 28, 28]               0\n","        MaxPool2d-24          [-1, 512, 14, 14]               0\n","           Conv2d-25          [-1, 512, 14, 14]       2,359,808\n","             ReLU-26          [-1, 512, 14, 14]               0\n","           Conv2d-27          [-1, 512, 14, 14]       2,359,808\n","             ReLU-28          [-1, 512, 14, 14]               0\n","           Conv2d-29          [-1, 512, 14, 14]       2,359,808\n","             ReLU-30          [-1, 512, 14, 14]               0\n","        MaxPool2d-31            [-1, 512, 7, 7]               0\n","AdaptiveAvgPool2d-32            [-1, 512, 7, 7]               0\n","           Linear-33                 [-1, 4096]     102,764,544\n","             ReLU-34                 [-1, 4096]               0\n","          Dropout-35                 [-1, 4096]               0\n","           Linear-36                 [-1, 4096]      16,781,312\n","             ReLU-37                 [-1, 4096]               0\n","          Dropout-38                 [-1, 4096]               0\n","           Linear-39                 [-1, 1000]       4,097,000\n","================================================================\n","Total params: 138,357,544\n","Trainable params: 138,357,544\n","Non-trainable params: 0\n","----------------------------------------------------------------\n","Input size (MB): 0.57\n","Forward/backward pass size (MB): 218.78\n","Params size (MB): 527.79\n","Estimated Total Size (MB): 747.15\n","----------------------------------------------------------------\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Mq1ETOZCwzrU","colab_type":"code","outputId":"3388ce2e-b775-4007-a554-f4b452e524e7","executionInfo":{"status":"error","timestamp":1565041883791,"user_tz":240,"elapsed":396,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":232}},"source":["# ResNet\n","\n","# https://zablo.net/blog/post/using-resnet-for-mnist-in-pytorch-tutorial/\n","\n","\n","def resnet18(pretrained=False, **kwargs):\n","    \"\"\"Constructs a ResNet-18 model.\n","    Args:\n","        pretrained (bool): If True, returns a model pre-trained on ImageNet\n","    \"\"\"\n","    model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)\n","    if pretrained:\n","        model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))\n","    return model\n","  \n","  \n","\n","def forward(self, x):\n","    x = self.conv1(x)\n","    x = self.bn1(x)\n","    ## ... skipped a few lines ...\n","    return x\n","  \n","self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)\n","\n","\n","\n","class MnistResNet(ResNet):\n","    def __init__(self):\n","        super(MnistResNet, self).__init__(BasicBlock, [2, 2, 2, 2], num_classes=10)\n","        self.conv1 = torch.nn.Conv2d(1, 64, \n","            kernel_size=(7, 7), \n","            stride=(2, 2), \n","            padding=(3, 3), bias=False)\n","        \n","    def forward(self, x):\n","        return torch.softmax(\n","            super(MnistResNet, self).forward(x), dim=-1)\n","\n","      \n","      \n","from torchvision.models.resnet import ResNet, BasicBlock\n","from torchvision.datasets import MNIST\n","from tqdm.autonotebook import tqdm\n","from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score\n","import inspect\n","import time\n","from torch import nn, optim\n","import torch\n","from torchvision.transforms import Compose, ToTensor, Normalize, Resize\n","from torch.utils.data import DataLoader\n","\n","\n","\n"],"execution_count":0,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-2-2e5f8fba3444>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mConv2d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m64\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkernel_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstride\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpadding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name 'nn' is not defined"]}]},{"cell_type":"code","metadata":{"id":"fvwgq7ECw_i5","colab_type":"code","outputId":"372ae30b-5b05-483c-9013-d51f93ff7cad","executionInfo":{"status":"error","timestamp":1565041968832,"user_tz":240,"elapsed":300,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":130}},"source":["# model:\n","model = YourModelHere()\n","\n","# params you need to specify:\n","epochs = 5\n","train_loader, val_loader = # put your data loader here\n","loss_function = nn.CrossEntropyLoss() # your loss function, cross entropy works well for multi-class problems\n","\n","# optimizer, I've used Adadelta, as it wokrs well without any magic numbers\n","optimizer = optim.Adadelta(model.parameters())\n","\n","start_ts = time.time()\n","device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n","\n","losses = []\n","batches = len(train_loader)\n","val_batches = len(val_loader)\n","\n","# loop for every epoch (training + evaluation)\n","for epoch in range(epochs):\n","    total_loss = 0\n","\n","    # progress bar (works in Jupyter notebook too!)\n","    progress = tqdm(enumerate(train_loader), desc=\"Loss: \", total=batches)\n","\n","    # ----------------- TRAINING  -------------------- \n","    # set model to training\n","    model.train()\n","    \n","    for i, data in progress:\n","        X, y = data[0].to(device), data[1].to(device)\n","        \n","        # training step for single batch\n","        model.zero_grad()\n","        outputs = model(X)\n","        loss = loss_function(outputs, y)\n","        loss.backward()\n","        optimizer.step()\n","\n","        # getting training quality data\n","        current_loss = loss.item()\n","        total_loss += current_loss\n","\n","        # updating progress bar\n","        progress.set_description(\"Loss: {:.4f}\".format(total_loss/(i+1)))\n","        \n","    # releasing unceseccary memory in GPU\n","    if torch.cuda.is_available():\n","        torch.cuda.empty_cache()\n","    \n","    # ----------------- VALIDATION  ----------------- \n","    val_losses = 0\n","    precision, recall, f1, accuracy = [], [], [], []\n","    \n","    # set model to evaluating (testing)\n","    model.eval()\n","    with torch.no_grad():\n","        for i, data in enumerate(val_loader):\n","            X, y = data[0].to(device), data[1].to(device)\n","\n","            outputs = model(X) # this get's the prediction from the network\n","\n","            val_losses += loss_function(outputs, y)\n","\n","            predicted_classes = torch.max(outputs, 1)[1] # get class from network's prediction\n","            \n","            # calculate P/R/F1/A metrics for batch\n","            for acc, metric in zip((precision, recall, f1, accuracy), \n","                                   (precision_score, recall_score, f1_score, accuracy_score)):\n","                acc.append(\n","                    calculate_metric(metric, y.cpu(), predicted_classes.cpu())\n","                )\n","          \n","    print(f\"Epoch {epoch+1}/{epochs}, training loss: {total_loss/batches}, validation loss: {val_losses/val_batches}\")\n","    print_scores(precision, recall, f1, accuracy, val_batches)\n","    losses.append(total_loss/batches) # for plotting learning curve\n","print(f\"Training time: {time.time()-start_ts}s\")"],"execution_count":0,"outputs":[{"output_type":"error","ename":"SyntaxError","evalue":"ignored","traceback":["\u001b[0;36m  File \u001b[0;32m\"<ipython-input-3-1feda64921d6>\"\u001b[0;36m, line \u001b[0;32m5\u001b[0m\n\u001b[0;31m    train_loader, val_loader = # put your data loader here\u001b[0m\n\u001b[0m                                                          ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"]}]},{"cell_type":"code","metadata":{"id":"z_SdFi7IxUVV","colab_type":"code","colab":{}},"source":["def calculate_metric(metric_fn, true_y, pred_y):\n","    # multi class problems need to have averaging method\n","    if \"average\" in inspect.getfullargspec(metric_fn).args:\n","        return metric_fn(true_y, pred_y, average=\"macro\")\n","    else:\n","        return metric_fn(true_y, pred_y)\n","    \n","def print_scores(p, r, f1, a, batch_size):\n","    # just an utility printing function\n","    for name, scores in zip((\"precision\", \"recall\", \"F1\", \"accuracy\"), (p, r, f1, a)):\n","        print(f\"\\t{name.rjust(14, ' ')}: {sum(scores)/batch_size:.4f}\")"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Zw9bbgKaxVrc","colab_type":"code","colab":{}},"source":["def get_data_loaders(train_batch_size, val_batch_size):\n","    mnist = MNIST(download=False, train=True, root=\".\").train_data.float()\n","    \n","    data_transform = Compose([ Resize((224, 224)),ToTensor(), Normalize((mnist.mean()/255,), (mnist.std()/255,))])\n","\n","    train_loader = DataLoader(MNIST(download=True, root=\".\", transform=data_transform, train=True),\n","                              batch_size=train_batch_size, shuffle=True)\n","\n","    val_loader = DataLoader(MNIST(download=False, root=\".\", transform=data_transform, train=False),\n","                            batch_size=val_batch_size, shuffle=False)\n","    return train_loader, val_loader"],"execution_count":0,"outputs":[]}]}